#include "models/models.h"

void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    // MoE
    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,        false);
    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared,   false);
    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm,  false);
    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func,   false);

    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
    ml.get_key_or_arr("laguna.attention.layer_types", hparams.is_swa_impl, hparams.n_layer(), false);

    ml.get_key("laguna.rope.swa.dimension_count", hparams.n_rot_swa,                false);
    ml.get_key("laguna.rope.swa.freq_base",       hparams.rope_freq_base_train_swa, false);
    ml.get_key("laguna.rope.scaling.beta_fast",   hparams.yarn_beta_fast,           false);
    ml.get_key("laguna.rope.scaling.beta_slow",   hparams.yarn_beta_slow,           false);

    type = LLM_TYPE_UNKNOWN;
}

void llama_model_laguna::load_arch_tensors(llama_model_loader &) {
    LLAMA_LOAD_LOCALS;

    const int64_t n_ff_exp        = hparams.n_ff_exp;
    const int64_t n_ff_shexp      = hparams.n_ff_shexp;

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

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
    if (output == NULL) {
        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
    }

    for (int i = 0; i < n_layer; ++i) {
        auto & layer = layers[i];

        const int64_t n_head_i    = hparams.n_head(i);
        const int64_t n_head_kv_i = hparams.n_head_kv(i);
        const int64_t n_embd_q    = n_embd_head_k * n_head_i;
        const int64_t n_embd_kv   = n_embd_head_k * n_head_kv_i;

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

        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_q},  0);
        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_kv}, 0);
        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_kv}, 0);
        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q, n_embd},  0);

        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE_LAGUNA, "weight", i), {n_embd, n_head_i}, 0);

        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);

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

        if (i < (int) hparams.n_layer_dense_lead) {
            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "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_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
        } else {
            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, TENSOR_NOT_REQUIRED);

            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);

            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
        }
    }
}

std::unique_ptr<llm_graph_context> llama_model_laguna::build_arch_graph(const llm_graph_params & params) const {
    return std::make_unique<graph>(*this, params);
}

llama_model_laguna::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());

    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));

    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    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) {
        ggml_tensor * inpSA = inpL;

        const int64_t n_head_il    = hparams.n_head(il);
        const int64_t n_head_kv_il = hparams.n_head_kv(il);
        const bool    is_swa       = hparams.is_swa(il);

        const int   rope_n_dims = hparams.n_rot(il);
        const float rope_base   = is_swa ? hparams.rope_freq_base_train_swa  : hparams.rope_freq_base_train;
        const float rope_scale  = is_swa ? hparams.rope_freq_scale_train_swa : hparams.rope_freq_scale_train;
        const float rope_ext    = is_swa ? 0.0f : 1.0f;
        const float rope_bfast  = hparams.yarn_beta_fast;
        const float rope_bslow  = hparams.yarn_beta_slow;

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

        // self-attention
        {
            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
            cb(Qcur, "Qcur", il);
            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
            cb(Kcur, "Kcur", il);
            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
            cb(Vcur, "Vcur", il);

            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur);
            cb(gate, "gate", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head_il,    n_tokens);
            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv_il, n_tokens);
            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv_il, n_tokens);

            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
            cb(Qcur, "Qcur_normed", il);
            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
            cb(Kcur, "Kcur_normed", il);

            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
                    rope_n_dims, rope_type, hparams.n_ctx_orig_yarn, rope_base, rope_scale,
                    rope_ext, hparams.rope_attn_factor, rope_bfast, rope_bslow);
            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
                    rope_n_dims, rope_type, hparams.n_ctx_orig_yarn, rope_base, rope_scale,
                    rope_ext, hparams.rope_attn_factor, rope_bfast, rope_bslow);

            cb(Qcur, "Qcur", il);
            cb(Kcur, "Kcur", il);
            cb(Vcur, "Vcur", il);

            cur = build_attn(inp_attn,
                    nullptr, nullptr, nullptr,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
            cb(cur, "attn_pregate", il);

            gate = ggml_softplus(ctx0, gate);
            cur  = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens);
            gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens);
            cur  = ggml_mul(ctx0, cur, gate);
            cur  = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens);
            cb(cur, "attn_gated", il);

            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
            cb(cur, "attn_out", 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);
        }

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

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

        if ((uint32_t) il < hparams.n_layer_dense_lead) {
            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_SILU, LLM_FFN_PAR, il);
            cb(cur, "ffn_out", il);
        } else {
            ggml_tensor * up_scale = nullptr;
            float expert_weights_scale = hparams.expert_weights_scale;

#if defined(GGML_USE_METAL)
            if (n_tokens >= 32 && ggml_is_quantized(model.layers[il].ffn_down_exps->type)) {
                // ggml-metal switches MUL_MAT_ID from its range-safe
                // matrix-vector kernel to FP16 matrix tiles at 32 tokens
                // (ne21_mm_id_min in ggml_metal_op_mul_mat_id). Laguna's routed
                // SwiGLU activations can overflow those tiles. Scale the linear
                // up branch and fold the inverse power-of-two factor into the
                // existing routing-weight scale. Revisit this guard if the
                // Metal dispatch threshold changes.
                constexpr float down_input_scale = 1.0f / 256.0f;
                up_scale = ggml_fill(ctx0,
                        model.layers[il].ffn_exp_probs_b, down_input_scale);
                expert_weights_scale /= down_input_scale;
            }
#endif

            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,
                    model.layers[il].ffn_exp_probs_b,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, hparams.expert_weights_norm,
                    expert_weights_scale,
                    (llama_expert_gating_func_type) hparams.expert_gating_func,
                    il,
                    nullptr, nullptr,
                    up_scale);
            cb(moe_out, "ffn_moe_out", il);

            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);
        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        cur = build_cvec(cur, il);
        cb(cur, "l_out", il);

        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;

    cur = build_lora_mm(model.output, cur, model.output_s);
    cb(cur, "result_output", -1);
    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);
}