Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_gemma3::load_arch_hparams(llama_model_loader & ml) {4    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);5    if (found_swa && hparams.n_swa > 0) {6        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;7        uint32_t swa_period = 6;8        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);9        hparams.set_swa_pattern(swa_period);10 11        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);12    } else {13        hparams.swa_type = LLAMA_SWA_TYPE_NONE;14    }15 16    hparams.f_final_logit_softcapping = 0.0f;17    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);18    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);19 20    switch (hparams.n_layer()) {21        case 18: type = LLM_TYPE_270M; break;22        case 26: type = LLM_TYPE_1B; break;23        case 32: type = LLM_TYPE_8B; break; // Rnj-124        case 34: type = LLM_TYPE_4B; break;25        case 48: type = LLM_TYPE_12B; break;26        case 62: type = LLM_TYPE_27B; break;27        default: type = LLM_TYPE_UNKNOWN;28    }29 30    // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L28931    hparams.f_attention_scale = type == LLM_TYPE_27B32        ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))33        : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));34}35 36void llama_model_gemma3::load_arch_tensors(llama_model_loader &) {37    LLAMA_LOAD_LOCALS;38 39    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);40 41    // output42    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);43    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);44 45    // if output is NULL, init from the input tok embed46    if (output == NULL) {47        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);48    }49 50    // Dense linear weights51    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);52    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);53 54 55    for (int i = 0; i < n_layer; ++i) {56        auto & layer = layers[i];57 58        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);59 60        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);61        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);62 63        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);64        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);65        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);66 67        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);68        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);69        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);70        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);71        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);72    }73}74 75std::unique_ptr<llm_graph_context> llama_model_gemma3::build_arch_graph(const llm_graph_params & params) const {76    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {77        return std::make_unique<graph<true>>(*this, params);78    } else {79        return std::make_unique<graph<false>>(*this, params);80    }81}82 83template <bool iswa>84llama_model_gemma3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {85    const int64_t n_embd_head = hparams.n_embd_head_k();86 87    ggml_tensor * cur;88    ggml_tensor * inpL;89 90    inpL = build_inp_embd(model.tok_embd);91 92    // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)93    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);94    cb(inpL, "inp_scaled", -1);95 96    // inp_pos - contains the positions97    ggml_tensor * inp_pos = build_inp_pos();98 99    // TODO: is causal == true correct? might need some changes100    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;101    inp_attn_type * inp_attn = nullptr;102 103    if constexpr (iswa) {104        inp_attn = build_attn_inp_kv_iswa();105    } else {106        inp_attn = build_attn_inp_kv();107    }108 109    ggml_tensor * inp_out_ids = build_inp_out_ids();110 111    for (int il = 0; il < n_layer; ++il) {112        float freq_base_l  = 0.0f;113        float freq_scale_l = 0.0f;114 115        if constexpr (iswa) {116            freq_base_l  = model.get_rope_freq_base (cparams, il);117            freq_scale_l = model.get_rope_freq_scale(cparams, il);118        } else {119            freq_base_l  = freq_base;120            freq_scale_l = freq_scale;121        }122 123        // norm124        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);125        cb(cur, "attn_norm", il);126 127        // self-attention128        {129            // compute Q and K and RoPE them130            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,131                    n_embd_head, n_head, n_head_kv, il);132 133            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);134            cb(Qcur, "Qcur_normed", il);135 136            Qcur = ggml_rope_ext(137                    ctx0, Qcur, inp_pos, nullptr,138                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,139                    ext_factor, attn_factor, beta_fast, beta_slow);140 141            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);142            cb(Kcur, "Kcur_normed", il);143 144            Kcur = ggml_rope_ext(145                    ctx0, Kcur, inp_pos, nullptr,146                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,147                    ext_factor, attn_factor, beta_fast, beta_slow);148 149            cb(Qcur, "Qcur", il);150            cb(Kcur, "Kcur", il);151            cb(Vcur, "Vcur", il);152 153            // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315154            Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);155 156            cur = build_attn(inp_attn,157                    model.layers[il].wo, NULL, model.layers[il].wo_s,158                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);159        }160        if (il == n_layer - 1 && inp_out_ids) {161            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);162            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);163        }164        cur = build_norm(cur,165                model.layers[il].attn_post_norm, NULL,166                LLM_NORM_RMS, il);167        cb(cur, "attn_post_norm", il);168 169        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);170        cb(sa_out, "sa_out", il);171 172        cur = build_norm(sa_out,173                model.layers[il].ffn_norm, NULL,174                LLM_NORM_RMS, il);175        cb(cur, "ffn_norm", il);176 177        // feed-forward network178        {179            cur = build_ffn(cur,180                    model.layers[il].ffn_up,   NULL, NULL,181                    model.layers[il].ffn_gate, NULL, NULL,182                    model.layers[il].ffn_down, NULL, NULL,183                    NULL,184                    LLM_FFN_GELU, LLM_FFN_PAR, il);185            cb(cur, "ffn_out", il);186        }187        cur = build_norm(cur,188                model.layers[il].ffn_post_norm, NULL,189                LLM_NORM_RMS, -1);190        cb(cur, "ffn_post_norm", il);191 192        cur = ggml_add(ctx0, cur, sa_out);193 194        cur = build_cvec(cur, il);195        cb(cur, "l_out", il);196 197        // input for next layer198        inpL = cur;199    }200    cur = inpL;201 202    cur = build_norm(cur,203            model.output_norm, NULL,204            LLM_NORM_RMS, -1);205 206    cb(cur, "result_norm", -1);207    res->t_embd = cur;208 209    // lm_head210    cur = build_lora_mm(model.output, cur, model.output_s);211 212    if (hparams.f_final_logit_softcapping) {213        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);214        cur = ggml_tanh(ctx0, cur);215        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);216    }217 218    cb(cur, "result_output", -1);219    res->t_logits = cur;220 221    ggml_build_forward_expand(gf, cur);222}223 224template struct llama_model_gemma3::graph<false>;225template struct llama_model_gemma3::graph<true>;226