#include "models.h"

void llama_model_gemma2::load_arch_hparams(llama_model_loader & ml) {

    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;

    hparams.n_swa = 4096; // default value of gemma 2

    uint32_t swa_period = 2;

    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);

    hparams.set_swa_pattern(swa_period);

    hparams.attn_soft_cap = true;

    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;

    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

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

    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,      hparams.f_attn_logit_softcapping, false);

    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);

    switch (hparams.n_layer()) {

        case 26: type = LLM_TYPE_2B; break;

        case 42: type = LLM_TYPE_9B; break;

        case 46: type = LLM_TYPE_27B; break;

        default: type = LLM_TYPE_UNKNOWN;

   }

    // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173

    hparams.f_attention_scale = type == LLM_TYPE_27B

        ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))

        : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));

}

void llama_model_gemma2::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

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

    // output

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

    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading

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

        auto & layer = layers[i];

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

        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);

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

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

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

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

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

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

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

    }

}

std::unique_ptr<llm_graph_context> llama_model_gemma2::build_arch_graph(const llm_graph_params & params) const {

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

}

llama_model_gemma2::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_k();

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));

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

    // inp_pos - contains the positions

    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) {

        const float freq_base_l  = model.get_rope_freq_base (cparams, il);

        const float freq_scale_l = model.get_rope_freq_scale(cparams, 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

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

                    n_embd_head, n_head, n_head_kv, il);

            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);

            Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);

            cur = build_attn(inp_attn,

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

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, 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);

        }

        cur = build_norm(cur,

                model.layers[il].attn_post_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "attn_post_norm", il);

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

        cb(sa_out, "sa_out", il);

        cur = build_norm(sa_out,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // feed-forward network

        {

            cur = build_ffn(cur,

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

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

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

                    NULL,

                    LLM_FFN_GELU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        }

        cur = build_norm(cur,

                model.layers[il].ffn_post_norm, NULL,

                LLM_NORM_RMS, -1);

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

        cur = ggml_add(ctx0, cur, sa_out);

        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, model.output_s);

    // final logit soft-capping

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

    cur = ggml_tanh(ctx0, cur);

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

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}