Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_neo_bert::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    if (hparams.n_layer() == 28) {7        type = LLM_TYPE_250M;8    }9}10 11void llama_model_neo_bert::load_arch_tensors(llama_model_loader &) {12    LLAMA_LOAD_LOCALS;13 14    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);15 16    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);17    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);18 19    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);20    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);21 22    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);23 24    for (int i = 0; i < n_layer; ++i) {25        auto & layer = layers[i];26 27        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);28 29        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);30        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);31 32        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);33 34        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff*2}, 0);35        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);36    }37}38 39std::unique_ptr<llm_graph_context> llama_model_neo_bert::build_arch_graph(const llm_graph_params & params) const {40    return std::make_unique<graph>(*this, params);41}42 43llama_model_neo_bert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {44    const int64_t n_embd_head = hparams.n_embd_head_v();45 46    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());47 48    ggml_tensor * cur;49    ggml_tensor * inpL;50    ggml_tensor * inp_pos = build_inp_pos();51 52    // construct input embeddings (token, type, position)53    inpL = build_inp_embd(model.tok_embd);54    cb(inpL, "inp_embd", -1);55 56    auto * inp_attn = build_attn_inp_no_cache();57 58    ggml_tensor * inp_out_ids = build_inp_out_ids();59 60    for (int il = 0; il < n_layer; ++il) {61        ggml_tensor * cur = inpL;62 63        // pre-norm64        cur = build_norm(inpL,65                model.layers[il].attn_norm, NULL,66                LLM_NORM_RMS, il);67 68        {69            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,70                    n_embd_head, n_head, n_head_kv, il);71 72            // RoPE73            Qcur = ggml_rope_ext(74                    ctx0, Qcur, inp_pos, nullptr,75                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,76                    ext_factor, attn_factor, beta_fast, beta_slow77                    );78 79            Kcur = ggml_rope_ext(80                    ctx0, Kcur, inp_pos, nullptr,81                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,82                    ext_factor, attn_factor, beta_fast, beta_slow83                    );84 85            cb(Qcur, "Qcur", il);86            cb(Kcur, "Kcur", il);87            cb(Vcur, "Vcur", il);88 89            cur = build_attn(inp_attn,90                    model.layers[il].wo, nullptr, model.layers[il].wo_s,91                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);92            cb(cur, "kqv_out", il);93        }94        if (il == n_layer - 1 && inp_out_ids) {95            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);96            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);97        }98        // re-add the layer input99        cur = ggml_add(ctx0, cur, inpL);100 101        ggml_tensor * ffn_inp = cur;102        cb(ffn_inp, "ffn_inp", il);103 104        // pre-norm105        cur = build_norm(ffn_inp,106                model.layers[il].ffn_norm, NULL,107                LLM_NORM_RMS, il);108        cb(cur, "ffn_norm", il);109 110        // feed-forward network111        cur = build_ffn(cur,112                model.layers[il].ffn_up,113                NULL, NULL, NULL, NULL, NULL,114                model.layers[il].ffn_down,115                NULL, NULL, NULL,116                LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);117 118        // attentions bypass the intermediate layer119        cur = ggml_add(ctx0, cur, ffn_inp);120 121        // input for next layer122        inpL = cur;123    }124    cur = inpL;125 126    cur = build_norm(cur,127            model.output_norm_enc, NULL,128            LLM_NORM_RMS, -1);129 130    cb(cur, "result_embd", -1);131    res->t_embd = cur;132 133    ggml_build_forward_expand(gf, cur);134}135