Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_olmo::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);5    ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);6 7    switch (hparams.n_layer()) {8        case 22: type = LLM_TYPE_1B; break;9        case 32: type = LLM_TYPE_7B; break;10        case 80: type = LLM_TYPE_70B; break;11        default: type = LLM_TYPE_UNKNOWN;12    }13}14 15void llama_model_olmo::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 = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);22    // if output is NULL, init from the input tok embed23    if (output == NULL) {24        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);25    }26 27    for (int i = 0; i < n_layer; ++i) {28        auto & layer = layers[i];29 30        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);31        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);32 33        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);34        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);35        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);36    }37}38 39std::unique_ptr<llm_graph_context> llama_model_olmo::build_arch_graph(const llm_graph_params & params) const {40    return std::make_unique<graph>(*this, params);41}42 43llama_model_olmo::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    GGML_ASSERT(n_embd_head == n_rot);48 49    ggml_tensor * cur;50    ggml_tensor * inpL;51 52    inpL = build_inp_embd(model.tok_embd);53 54    // inp_pos - contains the positions55    ggml_tensor * inp_pos = build_inp_pos();56 57    auto * inp_attn = build_attn_inp_kv();58 59    ggml_tensor * inp_out_ids = build_inp_out_ids();60 61    for (int il = 0; il < n_layer; ++il) {62        ggml_tensor * inpSA = inpL;63 64        // norm65        cur = build_norm(inpL,66                NULL, NULL,67                LLM_NORM, il);68        cb(cur, "attn_norm", il);69 70        // self-attention71        {72            // compute Q and K and RoPE them73            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,74                    n_embd_head, n_head, n_head_kv, il);75 76            Qcur = ggml_rope_ext(77                    ctx0, Qcur, inp_pos, nullptr,78                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,79                    ext_factor, attn_factor, beta_fast, beta_slow80                    );81 82            Kcur = ggml_rope_ext(83                    ctx0, Kcur, inp_pos, nullptr,84                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,85                    ext_factor, attn_factor, beta_fast, beta_slow86                    );87 88            cb(Qcur, "Qcur", il);89            cb(Kcur, "Kcur", il);90            cb(Vcur, "Vcur", il);91 92            cur = build_attn(inp_attn,93                    model.layers[il].wo, nullptr, model.layers[il].wo_s,94                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);95        }96        if (il == n_layer - 1 && inp_out_ids) {97            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);98            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);99        }100        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);101        cb(ffn_inp, "ffn_inp", il);102 103        // feed-forward network104        cur = build_norm(ffn_inp,105                NULL, NULL,106                LLM_NORM, il);107        cb(cur, "ffn_norm", il);108 109        cur = build_ffn(cur,110                model.layers[il].ffn_up,   NULL, NULL,111                model.layers[il].ffn_gate, NULL, NULL,112                model.layers[il].ffn_down, NULL, NULL,113                NULL,114                LLM_FFN_SILU, LLM_FFN_PAR, il);115        cb(cur, "ffn_out", il);116 117        cur = ggml_add(ctx0, cur, ffn_inp);118        cb(cur, "ffn_out", il);119 120        cur = build_cvec(cur, il);121        cb(cur, "l_out", il);122 123        // input for next layer124        inpL = cur;125    }126    cur = inpL;127 128    cur = build_norm(cur,129            NULL, NULL,130            LLM_NORM, -1);131 132    cb(cur, "result_norm", -1);133    res->t_embd = cur;134 135    // lm_head136    cur = build_lora_mm(model.output, cur, model.output_s);137 138    cb(cur, "result_output", -1);139    res->t_logits = cur;140 141    ggml_build_forward_expand(gf, cur);142}143