Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_gemma::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    switch (hparams.n_layer()) {7        case 18: type = LLM_TYPE_2B; break;8        case 28: type = LLM_TYPE_7B; break;9        default: type = LLM_TYPE_UNKNOWN;10   }11}12 13void llama_model_gemma::load_arch_tensors(llama_model_loader &) {14    LLAMA_LOAD_LOCALS;15 16    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);17 18    // output19    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);20    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading21 22    for (int i = 0; i < n_layer; ++i) {23        auto & layer = layers[i];24 25        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);26 27        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);28        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);29 30        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);31        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);32        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);33        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);34    }35}36 37std::unique_ptr<llm_graph_context> llama_model_gemma::build_arch_graph(const llm_graph_params & params) const {38    return std::make_unique<graph>(*this, params);39}40 41llama_model_gemma::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {42    const int64_t n_embd_head = hparams.n_embd_head_v();43 44    ggml_tensor * cur;45    ggml_tensor * inpL;46 47    inpL = build_inp_embd(model.tok_embd);48 49    inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));50    cb(inpL, "inp_scaled", -1);51 52    // inp_pos - contains the positions53    ggml_tensor * inp_pos = build_inp_pos();54 55    auto * inp_attn = build_attn_inp_kv();56 57    ggml_tensor * inp_out_ids = build_inp_out_ids();58 59    for (int il = 0; il < n_layer; ++il) {60        // norm61        cur = build_norm(inpL,62                model.layers[il].attn_norm, NULL,63                LLM_NORM_RMS, il);64        cb(cur, "attn_norm", il);65 66        // self-attention67        {68            // compute Q and K and RoPE them69            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,70                    n_embd_head, n_head, n_head_kv, il);71 72            Qcur = ggml_rope_ext(73                    ctx0, Qcur, inp_pos, nullptr,74                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,75                    ext_factor, attn_factor, beta_fast, beta_slow);76 77            Kcur = ggml_rope_ext(78                    ctx0, Kcur, inp_pos, nullptr,79                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,80                    ext_factor, attn_factor, beta_fast, beta_slow);81 82            cb(Qcur, "Qcur", il);83            cb(Kcur, "Kcur", il);84            cb(Vcur, "Vcur", il);85 86            Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));87            cb(Qcur, "Qcur_scaled", il);88 89            cur = build_attn(inp_attn,90                    model.layers[il].wo, NULL, model.layers[il].wo_s,91                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);92        }93        if (il == n_layer - 1 && inp_out_ids) {94            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);95            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);96        }97        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);98        cb(sa_out, "sa_out", il);99 100        cur = build_norm(sa_out,101                model.layers[il].ffn_norm, NULL,102                LLM_NORM_RMS, il);103        cb(cur, "ffn_norm", il);104 105        // feed-forward network106        {107            cur = build_ffn(cur,108                    model.layers[il].ffn_up,   NULL, NULL,109                    model.layers[il].ffn_gate, NULL, NULL,110                    model.layers[il].ffn_down, NULL, NULL,111                    NULL,112                    LLM_FFN_GELU, LLM_FFN_PAR, il);113            cb(cur, "ffn_out", il);114        }115        cur = ggml_add(ctx0, cur, sa_out);116 117        cur = build_cvec(cur, il);118        cb(cur, "l_out", il);119 120        // input for next layer121        inpL = cur;122    }123    cur = inpL;124 125    cur = build_norm(cur,126            model.output_norm, NULL,127            LLM_NORM_RMS, -1);128 129    cb(cur, "result_norm", -1);130    res->t_embd = cur;131 132    // lm_head133    cur = build_lora_mm(model.output, cur, model.output_s);134 135    cb(cur, "result_output", -1);136    res->t_logits = cur;137 138    ggml_build_forward_expand(gf, cur);139}140