Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_muse_glimmer::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);6    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);7    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);8 9    hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;10    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);11 12    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;13    uint32_t swa_period = 4;14    if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {15        hparams.set_swa_pattern(swa_period);16    } else {17        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());18    }19 20    switch (hparams.n_layer()) {21        case 52: type = LLM_TYPE_30B; break;22        default: type = LLM_TYPE_UNKNOWN;23    }24}25 26void llama_model_muse_glimmer::load_arch_tensors(llama_model_loader &) {27    LLAMA_LOAD_LOCALS;28 29    tok_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);30    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);31    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);32 33    for (int i = 0; i < n_layer; ++i) {34        auto & layer = layers[i];35 36        // Pre/post-attention norms (Muse Glimmer's `weight + 1` applied at conversion time).37        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);38        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);39 40        // Q/K/V/O projections.41        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);42        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);43 44        // QK-norm. Weights are synthesized at conversion time to absorb `qk_scale_factor`.45        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);46        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);47 48        // Attention output gate: sigmoid(gate) * attn_out before o_proj (same as afmoe).49        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);50 51        // Pre/post-FFN norms (FFN_PRE_NORM is aliased to LLM_TENSOR_FFN_NORM).52        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);53        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);54 55        // Dense FFN (unlike afmoe, no MoE branches).56        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);57        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);58        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);59    }60}61 62llama_model_muse_glimmer::graph::graph(const llama_model & model, const llm_graph_params & params)63    : llm_graph_context(params) {64    const int64_t n_embd_head = hparams.n_embd_head_v();65    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());66 67    // Different to f_norm_rms_eps for post-attn / post-FFN norms68    const float post_norm_eps = 1e-8f;69 70    ggml_tensor * cur;71    ggml_tensor * inpL;72 73    inpL = build_inp_embd(model.tok_embd);74    inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);75    cb(inpL, "embd_norm", -1);76 77    ggml_tensor * inp_pos = build_inp_pos();78    auto * inp_attn = build_attn_inp_kv_iswa();79    ggml_tensor * inp_out_ids = build_inp_out_ids();80 81    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));82 83    for (int il = 0; il < n_layer; ++il) {84        // expose per-layer residual for speculative drafts (see LLM_KV_TARGET_LAYERS).85        res->t_layer_inp[il] = inpL;86 87        const float freq_base_l  = model.get_rope_freq_base (cparams, il);88        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);89 90        ggml_tensor * inpSA = inpL;91 92        // RoPE runs on the SWA layers, NoPE on full ones.93        const bool use_rope = hparams.is_swa(il);94 95        // pre-attention norm (weight+1 folded at conversion time)96        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);97        cb(cur, "attn_norm", il);98 99        // self-attention: attention output gate around SDPA (afmoe.cpp:147-191)100        {101            ggml_tensor * attn_inp = cur;  // save input for gate computation102 103            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,104                    n_embd_head, n_head, n_head_kv, il);105 106            // gate = wqkv_gate @ attn_inp (from pre-attn hidden state)107            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);108            cb(gate, "attn_gate_proj", il);109 110            // QK-norm. attn_q_norm weight was synthesized at conversion to broadcast111            // qk_scale_factor across head_dim; attn_k_norm is identity (ones).112            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);113            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);114            cb(Qcur, "Qcur_normed", il);115            cb(Kcur, "Kcur_normed", il);116 117            if (use_rope) {118                Qcur = ggml_rope_ext(119                        ctx0, Qcur, inp_pos, nullptr,120                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,121                        ext_factor, attn_factor, beta_fast, beta_slow);122                cb(Qcur, "Qcur_rope", il);123 124                Kcur = ggml_rope_ext(125                        ctx0, Kcur, inp_pos, nullptr,126                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,127                        ext_factor, attn_factor, beta_fast, beta_slow);128                cb(Kcur, "Kcur_rope", il);129            }130 131            // SDPA. wo is deferred; the gate goes between attn_out and o_proj.132            cur = build_attn(inp_attn,133                    NULL, NULL, NULL,134                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);135            cb(cur, "attn_out", il);136 137            gate = ggml_sigmoid(ctx0, gate);138            cb(gate, "attn_gate_sig", il);139            cur = ggml_mul(ctx0, cur, gate);140            cb(cur, "attn_gated", il);141 142            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);143            cb(cur, "attn_o_proj", il);144        }145 146        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);147        cur = ggml_mul(ctx0, cur, model.layers[il].attn_post_norm);148        cb(cur, "attn_post_norm", il);149 150        if (il == n_layer - 1 && inp_out_ids) {151            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);152            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);153        }154 155        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);156        cb(ffn_inp, "ffn_inp", il);157 158        // pre-FFN norm159        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);160        cb(cur, "ffn_norm", il);161 162        // SwiGLU dense FFN163        cur = build_ffn(cur,164                model.layers[il].ffn_up,   NULL, NULL,165                model.layers[il].ffn_gate, NULL, NULL,166                model.layers[il].ffn_down, NULL, NULL,167                NULL,168                LLM_FFN_SILU, LLM_FFN_PAR, il);169        cb(cur, "ffn_out", il);170 171        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);172        cur = ggml_mul(ctx0, cur, model.layers[il].ffn_post_norm);173        cb(cur, "ffn_post_norm", il);174 175        cur = ggml_add(ctx0, cur, ffn_inp);176        cur = build_cvec(cur, il);177        cb(cur, "l_out", il);178 179        inpL = cur;180    }181 182    cur = inpL;183 184    // final norm185    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);186    cb(cur, "result_norm", -1);187    res->t_embd = cur;188 189    // lm_head, followed by output multiplier190    cur = build_lora_mm(model.output, cur, model.output_s);191    cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);192 193    // Final logit tanh softcap (from gemma3.cpp).194    if (hparams.f_final_logit_softcapping) {195        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);196        cur = ggml_tanh(ctx0, cur);197        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);198    }199 200    cb(cur, "result_output", -1);201    res->t_logits = cur;202 203    ggml_build_forward_expand(gf, cur);204}205 206std::unique_ptr<llm_graph_context> llama_model_muse_glimmer::build_arch_graph(const llm_graph_params & params) const {207    return std::make_unique<graph>(*this, params);208}209