llama.cpp/src/models/muse-glimmer.cpp

#include "models.h"

void llama_model_muse_glimmer::load_arch_hparams(llama_model_loader & ml) {
    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);

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

    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
    load_swa_pattern(ml, 4);

    switch (hparams.n_layer()) {
        case 52: type = LLM_TYPE_30B; break;
        default: type = LLM_TYPE_UNKNOWN;
    }
}

void llama_model_muse_glimmer::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_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; ++i) {
        auto & layer = layers[i];

        // Pre/post-attention norms (Muse Glimmer's `weight + 1` applied at conversion time).
        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);

        // Q/K/V/O projections.
        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);

        // QK-norm. Weights are synthesized at conversion time to absorb `qk_scale_factor`.
        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);

        // Attention output gate: sigmoid(gate) * attn_out before o_proj (same as afmoe).
        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);

        // Pre/post-FFN norms (FFN_PRE_NORM is aliased to LLM_TENSOR_FFN_NORM).
        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);
        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);

        // Dense FFN (unlike afmoe, no MoE branches).
        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);
    }
}

llama_model_muse_glimmer::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());

    // Different to f_norm_rms_eps for post-attn / post-FFN norms
    const float post_norm_eps = 1e-8f;

    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);
    inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);
    cb(inpL, "embd_norm", -1);

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

    for (int il = 0; il < n_layer; ++il) {
        // expose per-layer residual for speculative drafts (see LLM_KV_TARGET_LAYERS).
        res->t_layer_inp[il] = inpL;

        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 * inpSA = inpL;

        // RoPE runs on the SWA layers, NoPE on full ones.
        const bool use_rope = hparams.is_swa(il);

        // pre-attention norm (weight+1 folded at conversion time)
        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
        cb(cur, "attn_norm", il);

        // self-attention: attention output gate around SDPA (afmoe.cpp:147-191)
        {
            ggml_tensor * attn_inp = cur;  // save input for gate computation

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

            // gate = wqkv_gate @ attn_inp (from pre-attn hidden state)
            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
            cb(gate, "attn_gate_proj", il);

            // QK-norm. attn_q_norm weight was synthesized at conversion to broadcast
            // qk_scale_factor across head_dim; attn_k_norm is identity (ones).
            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);
            cb(Qcur, "Qcur_normed", il);
            cb(Kcur, "Kcur_normed", il);

            if (use_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);
                cb(Qcur, "Qcur_rope", il);

                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(Kcur, "Kcur_rope", il);
            }

            // SDPA. wo is deferred; the gate goes between attn_out and o_proj.
            cur = build_attn(inp_attn,
                    NULL, NULL, NULL,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
            cb(cur, "attn_out", il);

            gate = ggml_sigmoid(ctx0, gate);
            cb(gate, "attn_gate_sig", il);
            cur = ggml_mul(ctx0, cur, gate);
            cb(cur, "attn_gated", il);

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

        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
        cur = ggml_mul(ctx0, cur, model.layers[il].attn_post_norm);
        cb(cur, "attn_post_norm", 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);

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

        // SwiGLU dense FFN
        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);

        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
        cur = ggml_mul(ctx0, cur, model.layers[il].ffn_post_norm);
        cb(cur, "ffn_post_norm", il);

        cur = ggml_add(ctx0, cur, ffn_inp);
        cur = build_cvec(cur, il);
        cb(cur, "l_out", il);

        inpL = cur;
    }

    cur = inpL;

    // final norm
    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
    cb(cur, "result_norm", -1);
    res->t_embd = cur;

    // lm_head, followed by output multiplier
    cur = build_lora_mm(model.output, cur, model.output_s);
    cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);

    // Final logit tanh softcap (from gemma3.cpp).
    if (hparams.f_final_logit_softcapping) {
        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);
}

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