#include "models.h"

void llama_model_modern_bert::load_arch_hparams(llama_model_loader & ml) {

    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);

    if (found_swa && hparams.n_swa > 0) {

        hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;

        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);

        uint32_t swa_period = 3;

        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);

        hparams.set_swa_pattern(swa_period, true);

    } else {

        hparams.swa_type = LLAMA_SWA_TYPE_NONE;

    }

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

    // Some ModernBert derivatives (e.g. IBM Granite Embedding 97m R2) use

    // SiLU/SwiGLU in the FFN instead of the default GELU/GeGLU.

    hparams.llm_ffn_op = LLM_FFN_GEGLU;

    std::string hidden_act;

    if (ml.get_key(LLM_KV_HIDDEN_ACT, hidden_act, false)) {

        hparams.llm_ffn_op = llm_ffn_op_type_from_string(hidden_act, LLM_FFN_GEGLU);

    }

    switch (hparams.n_layer()) {

        case 12:

            type = LLM_TYPE_47M; break; // granite-embedding-small

        case 22:

            type = LLM_TYPE_149M; break; // modern-bert-base

        case 28:

            type = LLM_TYPE_395M; break; // modern-bert-large

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_modern_bert::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

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

        auto& layer = layers[i];

        if ( i != 0 ) {

            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

        } else{

            // layer 0 uses identity

            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);

        }

        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);

        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,   "weight", i), {n_embd, n_embd}, 0);

        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, 2 * n_ff}, 0);

        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);

        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

    }

    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);

    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);

    cls       = create_tensor(tn(LLM_TENSOR_CLS,      "weight"), {n_embd, n_embd},            TENSOR_NOT_REQUIRED);

    cls_norm  = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd},                    TENSOR_NOT_REQUIRED);

}

std::unique_ptr<llm_graph_context> llama_model_modern_bert::build_arch_graph(const llm_graph_params & params) const {

    return std::make_unique<graph>(*this, params);

}

llama_model_modern_bert::graph::graph(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_tensor * cur;

    ggml_tensor * inpL;

    ggml_tensor * inp_pos = build_inp_pos();

    // construct input embeddings (token, type, position)

    inpL = build_inp_embd(model.tok_embd);

    cb(inpL, "inp_embd", -1);

    // embed layer norm

    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, 0);

    cb(inpL, "inp_norm", 0);

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    auto * inp_attn = build_attn_inp_no_cache();

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

        // attention layer norm

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

            cur = build_norm(inpL,

                    model.layers[il].attn_norm, NULL,

                    LLM_NORM, il);

            cb(cur, "attn_norm", il);

        }

        // self attention

        auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,

                n_embd_head, n_head, n_head_kv, il);

        // RoPE

        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);

        cur = build_attn(inp_attn,

                    model.layers[il].wo, nullptr, model.layers[il].wo_s,

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);

        cb(cur, "kqv_out", il);

        if (il == n_layer - 1 && inp_out_ids) {

            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);

            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);

        }

        // re-add the layer input

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

        cb(ffn_inp, "ffn_inp", il);

        // attention layer norm

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM, il);

        cb(cur, "ffn_norm", il);

        cur = build_ffn(cur,

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

                NULL,                      NULL, NULL,

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

                NULL,

                hparams.llm_ffn_op,

                LLM_FFN_SEQ, il);

        // attentions bypass the intermediate layer

        cur = ggml_add(ctx0, cur, ffn_inp);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    cur = build_norm(cur,

            model.output_norm, NULL,

            LLM_NORM, -1);

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

    res->t_embd = cur;

    ggml_build_forward_expand(gf, cur);

}