Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_maincoder::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 32: type = LLM_TYPE_1B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_maincoder::load_arch_tensors(llama_model_loader &) {13    LLAMA_LOAD_LOCALS;14 15    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);16 17    // output18    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);19    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);20    // if output is NULL, init from the input tok embed21    if (output == NULL) {22        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);23    }24 25    for (int i = 0; i < n_layer; ++i) {26        auto & layer = layers[i];27 28        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);29 30        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);31        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);32 33        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);34        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);35 36        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);37        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);38        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);39        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);40    }41}42 43std::unique_ptr<llm_graph_context> llama_model_maincoder::build_arch_graph(const llm_graph_params & params) const {44    return std::make_unique<graph>(*this, params);45}46 47llama_model_maincoder::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {48    const int64_t n_embd_head = hparams.n_embd_head_v();49 50    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());51    GGML_ASSERT(n_embd_head == n_rot);52 53    ggml_tensor * cur;54    ggml_tensor * inpL;55 56    inpL = build_inp_embd(model.tok_embd);57 58    // inp_pos - contains the positions59    ggml_tensor * inp_pos = build_inp_pos();60 61    auto * inp_attn = build_attn_inp_kv();62 63    ggml_tensor * inp_out_ids = build_inp_out_ids();64 65    for (int il = 0; il < n_layer; ++il) {66        ggml_tensor * inpSA = inpL;67 68        // norm69        cur = build_norm(inpL,70                model.layers[il].attn_norm, NULL,71                LLM_NORM_RMS, il);72        cb(cur, "attn_norm", il);73 74        // self-attention75        {76            // compute Q and K and RoPE them77            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,78                    n_embd_head, n_head, n_head_kv, il);79 80            Qcur = ggml_rope_ext(81                    ctx0, Qcur, inp_pos, nullptr,82                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,83                    ext_factor, attn_factor, beta_fast, beta_slow84                    );85 86            Kcur = ggml_rope_ext(87                    ctx0, Kcur, inp_pos, nullptr,88                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,89                    ext_factor, attn_factor, beta_fast, beta_slow90                    );91 92            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);93            cb(Qcur, "Qcur_normed", il);94 95            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);96            cb(Kcur, "Kcur_normed", il);97 98            cb(Qcur, "Qcur", il);99            cb(Kcur, "Kcur", il);100            cb(Vcur, "Vcur", il);101 102            cur = build_attn(inp_attn,103                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,104                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);105        }106        if (il == n_layer - 1 && inp_out_ids) {107            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);108            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);109        }110        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);111        cb(ffn_inp, "ffn_inp", il);112 113        // feed-forward network114        cur = build_norm(ffn_inp,115                model.layers[il].ffn_norm, NULL,116                LLM_NORM_RMS, il);117        cb(cur, "ffn_norm", il);118 119        cur = build_ffn(cur,120                model.layers[il].ffn_up,   NULL, NULL,121                model.layers[il].ffn_gate, NULL, NULL,122                model.layers[il].ffn_down, NULL, NULL,123                NULL,124                LLM_FFN_SILU, LLM_FFN_PAR, il);125        cb(cur, "ffn_out", il);126 127        cur = ggml_add(ctx0, cur, ffn_inp);128 129        cur = build_cvec(cur, il);130        cb(cur, "l_out", il);131 132        // input for next layer133        inpL = cur;134    }135    cur = inpL;136 137    cur = build_norm(cur,138            model.output_norm, NULL,139            LLM_NORM_RMS, -1);140 141    cb(cur, "result_norm", -1);142    res->t_embd = cur;143 144    // lm_head145    cur = build_lora_mm(model.output, cur, model.output_s);146 147    cb(cur, "result_output", -1);148    res->t_logits = cur;149 150    ggml_build_forward_expand(gf, cur);151}152