#include "models.h"

void llama_model_gemma3n::load_arch_hparams(llama_model_loader & ml) {

    uint32_t swa_period = 5;

    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);

    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;

    hparams.set_swa_pattern(swa_period);

    hparams.n_layer_kv_from_start = 20;

    hparams.f_attention_scale     = 1.0f;

    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);

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    switch (hparams.n_layer()) {

        case 30: type = LLM_TYPE_E2B; break;

        case 35: type = LLM_TYPE_E4B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_gemma3n::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    const int64_t n_altup      = hparams.n_altup;

    const int64_t laurel_rank  = hparams.laurel_rank;

    const int64_t n_embd_altup = hparams.n_embd_altup;

    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

    // if output is NULL, init from the input tok embed

    if (output == NULL) {

        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

    }

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

    altup_proj        = create_tensor(tn(LLM_TENSOR_ALTUP_PROJ,        "weight"), {n_embd, n_embd, n_altup - 1}, 0);

    altup_unembd_proj = create_tensor(tn(LLM_TENSOR_ALTUP_UNEMBD_PROJ, "weight"), {n_embd, n_embd, n_altup - 1}, 0);

    per_layer_tok_embd   = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_altup * n_layer, n_vocab}, 0);

    per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_altup * n_layer}, 0);

    per_layer_proj_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM,  "weight", 0), {n_embd_altup}, 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];

        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_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.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);

        // altup & laurel

        layer.per_layer_inp_gate   = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE,  "weight", i), {n_embd, n_embd_altup}, 0);

        layer.per_layer_proj       = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ,      "weight", i), {n_embd_altup, n_embd}, 0);

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

        layer.altup_correct_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_COEF,  "weight", i), {n_altup, n_altup}, 0);

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

        layer.altup_predict_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_PREDICT_COEF,  "weight", i), {n_altup, n_altup * n_altup}, 0);

        layer.altup_router         = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER,        "weight", i), {n_embd, n_altup}, 0);

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

        layer.laurel_l             = create_tensor(tn(LLM_TENSOR_LAUREL_L,            "weight", i), {n_embd, laurel_rank}, 0);

        layer.laurel_r             = create_tensor(tn(LLM_TENSOR_LAUREL_R,            "weight", i), {laurel_rank, n_embd}, 0);

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

    }

}

std::unique_ptr<llm_graph_context> llama_model_gemma3n::build_arch_graph(const llm_graph_params & params) const {

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

}

// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim

static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {

    GGML_ASSERT(idx < (int) x->ne[2]);

    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),

                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));

}

llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_params & params) :

    llm_graph_context(params),

    model(model),

    n_embd_head(model.hparams.n_embd_head_k()),

    n_embd_altup(model.hparams.n_embd_altup),

    n_altup(model.hparams.n_altup),

    i_altup_act(model.hparams.i_altup_act) {

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)

    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);

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

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    // TODO: is causal == true correct? might need some changes

    auto * inp_attn = build_attn_inp_kv_iswa();

    ggml_tensor * inp_per_layer = build_inp_per_layer();

    ggml_build_forward_expand(gf, inp_per_layer);

    // inp_per_layer now has shape: [n_embd_altup, n_tokens, n_layer]

    inp_per_layer = project_per_layer_inputs(inpL, inp_per_layer);

    // inpL now has only 1 altup, project it to the rest of the altups

    // these "added" altups will be concat to the last dim of inpL

    {

        ggml_tensor * target_magnitude = calc_magnitude(inpL);

        ggml_tensor * inp_repeated     = ggml_repeat_4d(ctx0, inpL, n_embd, n_tokens, n_altup - 1, 1);

        ggml_tensor * altup_added =

            ggml_mul_mat(ctx0, model.altup_proj, inp_repeated);  // shape: [n_embd, n_tokens, n_altup - 1]

        ggml_tensor * new_magnitude = calc_magnitude(altup_added);

        altup_added                 = ggml_div(ctx0, ggml_mul(ctx0, altup_added, target_magnitude), new_magnitude);

        inpL                        = ggml_concat(ctx0, inpL, altup_added, 2);  // shape: [n_embd, n_tokens, n_altup]

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

    }

    // inpL now has shape: [n_embd, n_tokens, n_altup]

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

        // this block is made to be closely resemble Gemma3p5DecoderLayer on python code

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

        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);

        ggml_tensor * cur         = inpL;                    // [n_embd, n_tokens, n_altup]

        ggml_tensor * predictions = altup_predict(cur, il);  // [n_embd, n_tokens, n_altup]

        // predicted value will go through self-attention and laurel

        ggml_tensor * active_prediction = ggml_view_2d_slice(ctx0, predictions, i_altup_act);  // [n_embd, n_tokens]

        cur = active_prediction;

        cb(cur, "active_prediction", il);

        // norm

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

        cb(cur, "attn_norm", il);

        // laurel

        ggml_tensor * laurel_out = laurel(cur, il);  // [n_embd, n_tokens]

        // self-attention

        if (hparams.has_kv(il)) {

            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);

            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);

            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);

            Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);

            cb(Qcur, "Qcur_normed", il);

            cb(Kcur, "Kcur_normed", il);

            cb(Vcur, "Vcur_normed", 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_pos", il);

            cb(Kcur, "Kcur_pos", il);

            cur = build_attn(inp_attn, model.layers[il].wo,

                    NULL, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,

                    hparams.f_attention_scale, il);

        } else {

            // reuse KV cache of earlier layers

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

            cb(Qcur, "Qcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);

            cb(Qcur, "Qcur_normed", 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);

            cb(Qcur, "Qcur_pos", il);

            cur = build_attn(inp_attn,

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

                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);

        }

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

        cb(cur, "attn_post_norm", il);

        cur = ggml_add(ctx0, cur, active_prediction);  // [n_embd, n_tokens]

        cb(cur, "attn_gated", il);

        ggml_tensor * attn_laurel = ggml_scale(ctx0, ggml_add(ctx0, cur, laurel_out),

                                               1.0f / sqrtf(2.0f));  // [n_embd, n_tokens]

        cb(attn_laurel, "attn_laurel", il);

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

        cb(cur, "ffn_norm", il);

        // feed-forward network

        {

            ggml_tensor * up_proj   = build_lora_mm(model.layers[il].ffn_up, cur);

            ggml_tensor * gate_proj = build_lora_mm(model.layers[il].ffn_gate, cur);

            if (il < n_layer_sparsity) {

                // apply activation sparsity

                gate_proj = gaussian_topk(gate_proj);

            }

            gate_proj = ggml_gelu(ctx0, gate_proj);

            cur = ggml_mul(ctx0, up_proj, gate_proj);

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

            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", il);

        ggml_tensor * attn_ffw_laurel_gated = ggml_add(ctx0, cur, attn_laurel);  // [n_embd, n_tokens]

        cb(attn_ffw_laurel_gated, "attn_ffw_laurel_gated", il);

        ggml_tensor * corrected = altup_correct(predictions, attn_ffw_laurel_gated, il);  // [n_embd, n_tokens, n_altup]

        ggml_tensor * first_prediction;                                                   // [n_embd, n_tokens]

        {

            first_prediction = ggml_view_2d_slice(ctx0, corrected, i_altup_act);          // [n_embd, n_tokens]

            first_prediction = ggml_mul(ctx0, first_prediction, model.layers[il].altup_correct_scale);

            first_prediction = build_lora_mm(model.layers[il].per_layer_inp_gate, first_prediction);

            first_prediction = ggml_gelu(ctx0, first_prediction);                 // [n_embd_altup, n_tokens]

            cb(first_prediction, "first_prediction_gated", il);

            ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il);   // [n_embd_altup, n_tokens]

            first_prediction = ggml_mul(ctx0, first_prediction, inp_this_layer);  // [n_embd_altup, n_tokens]

            cb(first_prediction, "first_prediction_scaled", il);

            first_prediction = build_lora_mm(model.layers[il].per_layer_proj, first_prediction);  // [n_embd, n_tokens]

            first_prediction =

                build_norm(first_prediction, model.layers[il].per_layer_post_norm, NULL, LLM_NORM_RMS, il);

            cb(first_prediction, "first_prediction_out", il);

        }

        // equivalent to python code: corrected_predictions[1:] += first_prediction

        {

            ggml_tensor * slice_first = ggml_view_2d_slice(ctx0, corrected, 0);

            ggml_tensor * slice_rest  = ggml_view_3d(

                ctx0, corrected, n_embd, n_tokens, n_altup - 1, ggml_row_size(corrected->type, n_embd),

                ggml_row_size(corrected->type, n_embd * n_tokens), n_embd * n_tokens * ggml_element_size(corrected));

            ggml_tensor * tmp = ggml_add(ctx0, slice_rest, first_prediction);  // [n_embd, n_tokens, n_altup - 1]

            corrected         = ggml_concat(ctx0, slice_first, tmp, 2);        // [n_embd, n_tokens, n_altup]

        }

        cur = corrected;                                                       // [n_embd, n_tokens, n_altup]

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;  // [n_embd, n_tokens, n_altup]

    // cur now has multiple altup(s), we want to merge them back to 1 altup

    {

        ggml_tensor * target_magnitude = calc_magnitude(ggml_view_2d_slice(ctx0, cur, i_altup_act));  // [n_embd, n_tokens]

        // do a view to skip the first slice (active altup)

        ggml_tensor * alt_slice =

            ggml_view_3d(ctx0, cur, n_embd, n_tokens, n_altup - 1, ggml_row_size(cur->type, n_embd),

                         ggml_row_size(cur->type, n_embd * n_tokens), n_embd * n_tokens * ggml_element_size(cur));

        ggml_tensor * altup_unembd =

            ggml_mul_mat(ctx0, model.altup_unembd_proj, alt_slice);  // shape: [n_embd, n_tokens, n_altup - 1]

        ggml_tensor * new_magnitude = calc_magnitude(altup_unembd);

        altup_unembd                = ggml_div(ctx0, ggml_mul(ctx0, altup_unembd, target_magnitude), new_magnitude);

        cb(altup_unembd, "altup_unembd", -1);

        // equivalent to torch.mean(hidden_states, dim=0)

        cur = ggml_view_2d_slice(ctx0, cur, 0);  // [n_embd, n_tokens]

        for (int i = 0; i < n_altup - 1; ++i) {

            cur = ggml_add(ctx0, cur, ggml_view_2d_slice(ctx0, altup_unembd, i));

        }

        cur = ggml_scale(ctx0, cur, 1.0f / float(n_altup));  // [n_embd, n_tokens]

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

    }

    // cur now has shape: [n_embd, n_tokens]

    // TODO: move this to right after the last KV layer

    {

        // skip computing output for unused tokens

        ggml_tensor * inp_out_ids = build_inp_out_ids();

        cur                       = ggml_get_rows(ctx0, cur, inp_out_ids);

    }

    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);

    {

        // 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);

}

ggml_tensor * llama_model_gemma3n::graph::calc_magnitude(ggml_tensor * x) {

    return ggml_sqrt(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, x)));

}

// equivalent to get_per_layer_inputs() in python code

// output shape: [n_embd_altup, n_layer, n_tokens]

ggml_tensor * llama_model_gemma3n::graph::build_inp_per_layer() {

    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);

    ggml_tensor * inp_per_layer;

    float tok_embd_scale = sqrtf((float) n_embd_altup);

    if (ubatch.token) {

        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);

        ggml_set_input(inp->tokens);

        res->t_inp_tokens = inp->tokens;

        inp_per_layer = ggml_get_rows  (ctx0, model.per_layer_tok_embd, inp->tokens);

        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);

        inp_per_layer = ggml_scale     (ctx0, inp_per_layer, tok_embd_scale);

        cb(inp_per_layer, "inp_per_layer_selected", -1);

        res->add_input(std::move(inp));

    } else {

        // Multimodal embedding path: use padding token (ID=0) embedding

        // TODO: verify if this is the correct behavior in transformers implementation

        const int64_t embd_size = model.per_layer_tok_embd->ne[0];  // n_embd_altup * n_layer

        // Extract and dequantize padding token embedding (row 0)

        ggml_tensor * padding = ggml_view_1d(ctx0, model.per_layer_tok_embd, embd_size, 0);

        inp_per_layer = ggml_cast (ctx0, padding, GGML_TYPE_F32);

        inp_per_layer = ggml_scale(ctx0, inp_per_layer, tok_embd_scale);

        // Reshape to [n_embd_altup, n_layer, 1]

        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, 1);

        cb(inp_per_layer, "inp_per_layer_multimodal", -1);

    }

    return inp_per_layer;

}

// equivalent to project_per_layer_inputs() in python code

// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim

// output shape: [n_embd_altup, n_tokens, n_layer]

ggml_tensor * llama_model_gemma3n::graph::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {

    const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);

    const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);

    ggml_tensor * per_layer_proj;

    per_layer_proj = ggml_mul_mat   (ctx0, model.per_layer_model_proj, inp_batch);

    per_layer_proj = ggml_scale     (ctx0, per_layer_proj, per_layer_projection_scale);

    per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_altup, n_layer, n_tokens);

    per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, NULL, LLM_NORM_RMS, -1);

    cb(per_layer_proj, "per_layer_proj", -1);

    inp_per_layer = ggml_add  (ctx0, per_layer_proj, inp_per_layer);

    inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);

    cb(inp_per_layer, "inp_per_layer", -1);

    // permute to shape: [n_embd_altup, n_tokens, n_layer]

    inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));

    return inp_per_layer;

}

// input cur shape: [n_altup, n_tokens]

// output    shape: [n_altup, n_tokens]

ggml_tensor * llama_model_gemma3n::graph::laurel(ggml_tensor * cur, int il) {

    ggml_tensor * tmp = cur;

    tmp               = build_lora_mm(model.layers[il].laurel_l, tmp);

    tmp               = build_lora_mm(model.layers[il].laurel_r, tmp);

    tmp               = build_norm(tmp, model.layers[il].laurel_post_norm, NULL, LLM_NORM_RMS, il);

    tmp               = ggml_add(ctx0, tmp, cur);

    cb(tmp, "laurel_out", il);

    return tmp;

}

// input x shape: [n_embd, n_tokens]

// output  shape: [n_embd, n_tokens]

ggml_tensor * llama_model_gemma3n::graph::gaussian_topk(ggml_tensor * x) {

    ggml_tensor * mean = ggml_mean(ctx0, x);

    ggml_tensor * std  = ggml_sqrt(ctx0, ggml_scale(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x, mean))),

                                                    1.0f / (float) (x->ne[0] - 1)));

    ggml_tensor * cutoff_x = ggml_add(ctx0, mean, ggml_scale(ctx0, std, f_sparsity_std_mul));

    return ggml_relu(ctx0, ggml_sub(ctx0, x, cutoff_x));

}

//

// altup functions

//

// equivalent to compute_router_modalities() in python code

// input x shape: [n_embd,  n_tokens]

// output  shape: [n_altup, n_tokens]

ggml_tensor * llama_model_gemma3n::graph::altup_compute_router_modalities(ggml_tensor * x, int il) {

    ggml_tensor * router_inputs = build_norm(x, model.layers[il].altup_router_norm, NULL, LLM_NORM_RMS, il);

    // router_input_scale

    router_inputs = ggml_scale(ctx0, router_inputs, 1.0f / (float) n_embd);

    ggml_tensor * output = ggml_mul_mat(ctx0, model.layers[il].altup_router, router_inputs);

    return ggml_tanh(ctx0, output);  // [n_altup, n_tokens]

}

// input cur shape: [n_embd, n_tokens, n_altup]

// output    shape: [n_embd, n_tokens, n_altup]

ggml_tensor * llama_model_gemma3n::graph::altup_predict(ggml_tensor * cur, int il) {

    ggml_tensor * activated  = ggml_view_2d_slice(ctx0, cur, i_altup_act);      // [n_embd, n_tokens]

    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]

    cb(modalities, "modalities", il);

    ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_predict_coef, modalities);

    cb(all_coefs, "all_coefs", il);

    // first dim now having n_altup^2 elements, we reshape it to 2D (so we end up with 3D tensor)

    all_coefs = ggml_reshape_3d(ctx0, all_coefs, n_altup, n_altup, n_tokens);

    // permute to [n_altup, n_embd, n_tokens]

    ggml_tensor * cur_permuted = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));

    ggml_tensor * predictions  = ggml_mul_mat(ctx0, cur_permuted, all_coefs);  // [n_altup, n_embd, n_tokens]

    // final shape must be the same as cur: [n_embd, n_tokens, n_altup]

    predictions = ggml_cont(ctx0, ggml_permute(ctx0, predictions, 0, 2, 1, 3));

    predictions = ggml_add(ctx0, predictions, cur);

    cb(predictions, "predictions", il);

    return predictions;

}

// input predictions       shape: [n_embd, n_tokens, n_altup]

// input activated         shape: [n_embd, n_tokens]

// output                  shape: [n_embd, n_tokens, n_altup]

ggml_tensor * llama_model_gemma3n::graph::altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {

    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]

    cb(modalities, "modalities", il);

    ggml_tensor * active_prediction = ggml_view_2d_slice(ctx0, predictions, i_altup_act);

    ggml_tensor * innovation        = ggml_sub(ctx0, activated, active_prediction);  // [n_embd, n_tokens]

    cb(innovation, "innovation", il);

    ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_correct_coef, modalities);  // [n_altup, n_tokens]

    all_coefs               = ggml_scale_bias(ctx0, all_coefs, 1.0f, 1.0f);                    // + 1.0

    cb(all_coefs, "all_coefs", il);

    all_coefs = ggml_transpose(ctx0, all_coefs);                                               // [n_tokens, n_altup]

    all_coefs = ggml_cont_3d(ctx0, all_coefs, 1, n_tokens, n_altup);                           // [1, n_tokens, n_altup]

    innovation              = ggml_repeat_4d(ctx0, innovation, n_embd, n_tokens, n_altup, 1);

    ggml_tensor * corrected = ggml_mul(ctx0, innovation, all_coefs);   // [n_embd, n_tokens, n_altup]

    corrected               = ggml_add(ctx0, corrected, predictions);  // [n_embd, n_tokens, n_altup]

    cb(corrected, "corrected", il);

    return corrected;

}