Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_openelm::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 16: type = LLM_TYPE_270M; break;8        case 20: type = LLM_TYPE_450M; break;9        case 28: type = LLM_TYPE_1B; break;10        case 36: type = LLM_TYPE_3B; break;11        default: type = LLM_TYPE_UNKNOWN;12    }13}14 15void llama_model_openelm::load_arch_tensors(llama_model_loader &) {16    LLAMA_LOAD_LOCALS;17 18    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);19 20    // output21    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);22    // init output from the input tok embed23    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);24 25    for (int i = 0; i < n_layer; ++i) {26        const int64_t n_head      =   hparams.n_head(i);27        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;28        const int64_t n_ff        =   hparams.n_ff(i);29 30        auto & layer = layers[i];31 32        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);33 34        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);35        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);36        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);37        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);38 39        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);40        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);41        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);42        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);43    }44}45 46std::unique_ptr<llm_graph_context> llama_model_openelm::build_arch_graph(const llm_graph_params & params) const {47    return std::make_unique<graph>(*this, params);48}49 50llama_model_openelm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {51    const int64_t n_embd_head = hparams.n_embd_head_v();52 53    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());54 55    ggml_tensor * cur;56    ggml_tensor * inpL;57    inpL = build_inp_embd(model.tok_embd);58 59    // inp_pos - contains the positions60    ggml_tensor * inp_pos = build_inp_pos();61 62    auto * inp_attn = build_attn_inp_kv();63 64    ggml_tensor * inp_out_ids = build_inp_out_ids();65 66    for (int il = 0; il < n_layer; ++il) {67        const int64_t n_head    = hparams.n_head(il);68        const int64_t n_head_kv = hparams.n_head_kv(il);69        const int64_t n_head_qkv = 2*n_head_kv + n_head;70 71        cur = inpL;72        ggml_tensor * residual = cur;73 74        // norm75        cur = build_norm(inpL,76                model.layers[il].attn_norm, NULL,77                LLM_NORM_RMS, il);78        cb(cur, "attn_norm", il);79 80        // self-attention81        {82            cur = build_lora_mm(model.layers[il].wqkv, cur);83            cb(cur, "wqkv", il);84 85            cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);86 87            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, cur->nb[1], cur->nb[2], 0);88            cb(Qcur, "Qcur", il);89 90            ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head);91            cb(Kcur, "Kcur", il);92 93            ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv));94            cb(Vcur, "Vcur", il);95 96            Qcur = build_norm(Qcur,97                    model.layers[il].attn_q_norm, NULL,98                    LLM_NORM_RMS, il);99            cb(Qcur, "Qcur", il);100 101            Kcur = build_norm(Kcur,102                    model.layers[il].attn_k_norm, NULL,103                    LLM_NORM_RMS, il);104            cb(Kcur, "Kcur", il);105 106            Qcur = ggml_rope_ext(107                    ctx0, Qcur, inp_pos, NULL,108                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,109                    ext_factor, attn_factor, beta_fast, beta_slow110                    );111 112            Kcur = ggml_rope_ext(113                    ctx0, Kcur, inp_pos, NULL,114                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,115                    ext_factor, attn_factor, beta_fast, beta_slow116                    );117 118            cb(Qcur, "Qcur", il);119            cb(Kcur, "Kcur", il);120            cb(Qcur, "Vcur", il);121 122            cur = build_attn(inp_attn,123                    model.layers[il].wo, NULL, model.layers[il].wo_s,124                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);125        }126        if (il == n_layer - 1 && inp_out_ids) {127            residual = ggml_get_rows(ctx0, residual, inp_out_ids);128            cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);129        }130        ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);131        cb(ffn_inp, "ffn_inp", il);132 133        // feed-forward network134        {135            cur = build_norm(ffn_inp,136                    model.layers[il].ffn_norm, NULL,137                    LLM_NORM_RMS, il);138            cb(cur, "ffn_norm", il);139 140            cur = build_ffn(cur,141                    model.layers[il].ffn_up,   NULL, NULL,142                    model.layers[il].ffn_gate, NULL, NULL,143                    model.layers[il].ffn_down, NULL, NULL,144                    NULL,145                    LLM_FFN_SILU, LLM_FFN_PAR, il);146            cb(cur, "ffn_out", il);147        }148        cur = ggml_add(ctx0, cur, ffn_inp);149 150        cur = build_cvec(cur, il);151        cb(cur, "l_out", il);152 153        inpL = cur;154    }155    cur = inpL;156 157    // norm158    cur = build_norm(cur,159            model.output_norm, NULL,160            LLM_NORM_RMS, -1);161 162    cb(cur, "result_norm", -1);163    res->t_embd = cur;164 165    cur = build_lora_mm(model.output, cur, model.output_s);166 167    cb(cur, "result_output", -1);168    res->t_logits = cur;169 170    ggml_build_forward_expand(gf, cur);171}172