#include "models.h"

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

    llm_graph_context_mamba(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);

    auto * inp = build_inp_mem_hybrid();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    // Positional embeddings populated if rope enabled

    ggml_tensor * inp_pos = nullptr;

    if (hparams.rope_finetuned) {

        inp_pos = build_inp_pos();

    }

    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 {

            // attention layer //

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

        }

        // ffn

        cur = build_layer_ffn(cur, inpSA, model, 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);

    // For Granite architectures - scale logits

    if (hparams.f_logit_scale) {

        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);

    }

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

ggml_tensor * llm_build_granite_hybrid::build_attention_layer(ggml_tensor *             cur,

                                                              ggml_tensor *             inp_pos,

                                                              llm_graph_input_attn_kv * inp_attn,

                                                              const llama_model &       model,

                                                              const int64_t             n_embd_head,

                                                              const int                 il) {

    // compute Q and K and (optionally) RoPE them

    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);

    const bool use_rope = hparams.rope_finetuned;

    if (use_rope) {

        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);

        Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);

    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_granite_hybrid::build_layer_ffn(ggml_tensor *       cur,

                                                        ggml_tensor *       inpSA,

                                                        const llama_model & model,

                                                        const int           il) {

    // For Granite architectures - scale residual

    if (hparams.f_residual_scale) {

        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);

    }

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

    cb(ffn_inp, "ffn_inp", il);

    // feed-forward network (non-MoE)

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

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

        cb(cur, "ffn_norm", il);

        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_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        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,

                nullptr,

                n_expert, n_expert_used,

                LLM_FFN_SILU, true,

                false, 0.0,

                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,

                il);

        cb(moe_out, "ffn_moe_out", il);

        // For Granite MoE Shared

        if (hparams.n_ff_shexp > 0) {

            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);

        } else {

            cur = moe_out;

        }

    }

    // For Granite architectures - scale residual

    if (hparams.f_residual_scale) {

        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);

    }

    cur = ggml_add(ctx0, cur, ffn_inp);

    cb(cur, "ffn_out", il);

    cur = build_cvec(cur, il);

    cb(cur, "l_out", il);

    return cur;

}