Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_modern_bert::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_SYMMETRIC;7        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);8        uint32_t swa_period = 3;9        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);10        hparams.set_swa_pattern(swa_period, true);11    } else {12        hparams.swa_type = LLAMA_SWA_TYPE_NONE;13    }14 15    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);16 17    // Some ModernBert derivatives (e.g. IBM Granite Embedding 97m R2) use18    // SiLU/SwiGLU in the FFN instead of the default GELU/GeGLU.19    hparams.llm_ffn_op = LLM_FFN_GEGLU;20    std::string hidden_act;21    if (ml.get_key(LLM_KV_HIDDEN_ACT, hidden_act, false)) {22        hparams.llm_ffn_op = llm_ffn_op_type_from_string(hidden_act, LLM_FFN_GEGLU);23    }24 25    switch (hparams.n_layer()) {26        case 12:27            type = LLM_TYPE_47M; break; // granite-embedding-small28        case 22:29            type = LLM_TYPE_149M; break; // modern-bert-base30        case 28:31            type = LLM_TYPE_395M; break; // modern-bert-large32        default: type = LLM_TYPE_UNKNOWN;33    }34}35 36void llama_model_modern_bert::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    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);41 42    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);43 44    for(int i = 0; i < n_layer; ++i) {45        auto& layer = layers[i];46 47        if ( i != 0 ) {48            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);49        } else{50            // layer 0 uses identity51            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);52        }53 54 55        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);56        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,   "weight", i), {n_embd, n_embd}, 0);57 58        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, 2 * n_ff}, 0);59        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);60        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);61    }62 63    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);64    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);65    cls       = create_tensor(tn(LLM_TENSOR_CLS,      "weight"), {n_embd, n_embd},            TENSOR_NOT_REQUIRED);66    cls_norm  = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd},                    TENSOR_NOT_REQUIRED);67 68}69 70std::unique_ptr<llm_graph_context> llama_model_modern_bert::build_arch_graph(const llm_graph_params & params) const {71    return std::make_unique<graph>(*this, params);72}73 74llama_model_modern_bert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {75    const int64_t n_embd_head = hparams.n_embd_head_v();76 77    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());78 79    ggml_tensor * cur;80    ggml_tensor * inpL;81    ggml_tensor * inp_pos = build_inp_pos();82 83    // construct input embeddings (token, type, position)84    inpL = build_inp_embd(model.tok_embd);85    cb(inpL, "inp_embd", -1);86 87    // embed layer norm88    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, 0);89    cb(inpL, "inp_norm", 0);90 91    ggml_tensor * inp_out_ids = build_inp_out_ids();92 93    auto * inp_attn = build_attn_inp_no_cache();94 95    for (int il = 0; il < n_layer; ++il) {96        const float freq_base_l  = model.get_rope_freq_base(cparams, il);97        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);98 99        cur = inpL;100 101        // attention layer norm102        if (model.layers[il].attn_norm) {103            cur = build_norm(inpL,104                    model.layers[il].attn_norm, NULL,105                    LLM_NORM, il);106            cb(cur, "attn_norm", il);107        }108 109        // self attention110        auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,111                n_embd_head, n_head, n_head_kv, il);112 113        // RoPE114        Qcur = ggml_rope_ext(115                ctx0, Qcur, inp_pos, nullptr,116                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,117                ext_factor, attn_factor, beta_fast, beta_slow118                );119 120        Kcur = ggml_rope_ext(121                ctx0, Kcur, inp_pos, nullptr,122                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,123                ext_factor, attn_factor, beta_fast, beta_slow124                );125 126        cb(Qcur, "Qcur", il);127        cb(Kcur, "Kcur", il);128        cb(Vcur, "Vcur", il);129 130        cur = build_attn(inp_attn,131                    model.layers[il].wo, nullptr, model.layers[il].wo_s,132                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);133        cb(cur, "kqv_out", il);134 135        if (il == n_layer - 1 && inp_out_ids) {136            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);137            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);138        }139 140        // re-add the layer input141        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);142        cb(ffn_inp, "ffn_inp", il);143 144        // attention layer norm145        cur = build_norm(ffn_inp,146                model.layers[il].ffn_norm, NULL,147                LLM_NORM, il);148        cb(cur, "ffn_norm", il);149 150        cur = build_ffn(cur,151                model.layers[il].ffn_up,   NULL, NULL,152                NULL,                      NULL, NULL,153                model.layers[il].ffn_down, NULL, NULL,154                NULL,155                hparams.llm_ffn_op,156                LLM_FFN_SEQ, il);157 158        // attentions bypass the intermediate layer159        cur = ggml_add(ctx0, cur, ffn_inp);160 161        // input for next layer162        inpL = cur;163    }164 165    cur = inpL;166 167    cur = build_norm(cur,168            model.output_norm, NULL,169            LLM_NORM, -1);170    cb(cur, "final_norm_out", -1);171 172    res->t_embd = cur;173    ggml_build_forward_expand(gf, cur);174}175