#include "models.h"

void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;

    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);

    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,   hparams.n_swa);

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

    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());

    float value_scale = 0.0f;

    if (ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, value_scale, false) && value_scale != 1.0f) {

        hparams.f_attn_value_scale = value_scale;

    }

    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);

    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");

    switch (hparams.n_layer()) {

        case 48: type = LLM_TYPE_310B_A15B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_mimo2::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_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

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

        auto & layer = layers[i];

        uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);

        uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);

        uint32_t n_head = hparams.n_head(i);

        // NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support

        const bool is_nextn = i >= n_layer;

        const int  skip     = is_nextn ? TENSOR_SKIP : 0;

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

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

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

        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);

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

        // non-MoE branch

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

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

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

        // MoE branch

        int64_t n_ff_exp = hparams.n_ff_exp;

        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);

        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);

        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);

        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);

        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);

        if (is_nextn) {

            layer.nextn.eh_proj  = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);

            layer.nextn.enorm    = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, skip);

            layer.nextn.hnorm    = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, skip);

            layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);

        }

    }

}

std::unique_ptr<llm_graph_context> llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const {

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

}

llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    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();

    const float v_scale = hparams.f_attn_value_scale;

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

        ggml_tensor * inpSA = inpL;

        uint32_t n_head_l    = hparams.n_head(il);

        uint32_t n_head_kv_l = hparams.n_head_kv(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;

        // self_attention

        {

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

            cb(cur, "attn_norm", il);

            ggml_tensor * Qcur;

            ggml_tensor * Kcur;

            ggml_tensor * Vcur;

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

                // Fused qkv_proj - Q/K share head_dim_k, V uses head_dim_v

                ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);

                cb(qkv, "wqkv", il);

                const size_t row_k    = ggml_row_size(qkv->type, n_embd_head_k);

                const size_t row_v    = ggml_row_size(qkv->type, n_embd_head_v);

                const size_t row_full = qkv->nb[1];

                const size_t k_off    = row_k * n_head_l;

                const size_t v_off    = k_off + row_k * n_head_kv_l;

                Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l,    n_tokens, row_k, row_full, 0);

                Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);

                Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);

            } else {

                // Split path

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

                cb(Qcur, "Qcur", il);

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

                cb(Kcur, "Kcur", il);

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

                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);

                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);

                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);

            }

            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);

            ggml_tensor * sinks = model.layers[il].attn_sinks;

            cur = build_attn(inp_attn,

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

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

            cb(cur, "attn_out", il);

            if (v_scale) {

                cur = ggml_scale(ctx0, cur, v_scale);

                cb(cur, "attn_out_scaled", 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);

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // feed-forward network

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

            // dense branch

            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_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, true,

                    hparams.expert_weights_scale,

                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,

                    il);

            cb(cur, "ffn_moe_out", il);

        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        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);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}