Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_talkie::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);6 7    switch (hparams.n_layer()) {8        case 40: type = LLM_TYPE_13B; break;9        default: type = LLM_TYPE_UNKNOWN;10    }11}12 13void llama_model_talkie::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    output   = create_tensor(tn(LLM_TENSOR_OUTPUT,     "weight"), {n_embd, n_vocab}, 0);18 19    for (int i = 0; i < n_layer; ++i) {20        auto & layer = layers[i];21 22        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);23        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);24 25        // no k gain26        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {1, n_head}, 0);27 28        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);29        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);30        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);31 32        layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1}, 0);33    }34}35 36std::unique_ptr<llm_graph_context> llama_model_talkie::build_arch_graph(const llm_graph_params & params) const {37    return std::make_unique<graph>(*this, params);38}39 40llama_model_talkie::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {41    const int64_t n_embd_head = hparams.n_embd_head_k();42 43    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());44    GGML_ASSERT(n_embd_head == n_rot);45 46    ggml_tensor * cur;47    ggml_tensor * inpL;48 49    inpL = build_inp_embd(model.tok_embd);50    inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);51    cb(inpL, "inp_norm", -1);52 53    ggml_tensor * embd_skip = inpL;54 55    // inp_pos - contains the positions56    ggml_tensor * inp_pos = build_inp_pos();57 58    auto * inp_attn = build_attn_inp_kv();59 60    ggml_tensor * inp_out_ids = build_inp_out_ids();61 62    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));63 64    for (int il = 0; il < n_layer; ++il) {65        ggml_tensor * inpSA = inpL;66        ggml_tensor * inp_skip = embd_skip;67 68        cur = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, il);69        cb(cur, "attn_norm", il);70 71        // self-attention72        {73            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_slow);80 81            Kcur = ggml_rope_ext(82                    ctx0, Kcur, inp_pos, nullptr,83                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,84                    ext_factor, attn_factor, beta_fast, beta_slow);85 86            // reference applies qknorm after rope87            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);88            cb(Qcur, "Qcur_norm", il);89 90            Kcur = build_norm(Kcur, nullptr, nullptr, LLM_NORM_RMS, il);91            cb(Kcur, "Kcur_norm", il);92 93            cb(Vcur, "Vcur", il);94 95            cur = build_attn(inp_attn,96                    model.layers[il].wo, nullptr, model.layers[il].wo_s,97                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);98            cb(cur, "attn_out", il);99        }100 101        if (il == n_layer - 1 && inp_out_ids) {102            cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);103            inpSA    = ggml_get_rows(ctx0, inpSA,    inp_out_ids);104            inp_skip = ggml_get_rows(ctx0, inp_skip, inp_out_ids);105        }106 107        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);108        cb(ffn_inp, "ffn_inp", il);109 110        cur = build_norm(ffn_inp, nullptr, nullptr, LLM_NORM_RMS, il);111        cb(cur, "ffn_norm", il);112 113        cur = build_ffn(cur,114                model.layers[il].ffn_up,   nullptr, nullptr,115                model.layers[il].ffn_gate, nullptr, nullptr,116                model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,117                nullptr,118                LLM_FFN_SILU, LLM_FFN_PAR, il);119        cb(cur, "ffn_out", il);120 121        cur = ggml_add(ctx0, cur, ffn_inp);122 123        ggml_tensor * skip = ggml_mul(ctx0, inp_skip, model.layers[il].out_scale);124        cb(skip, "embd_skip", il);125 126        cur = ggml_add(ctx0, cur, skip);127 128        cur = build_cvec(cur, il);129        cb(cur, "l_out", il);130 131        // input for next layer132        inpL = cur;133    }134 135    cur = inpL;136 137    cur = build_norm(cur, nullptr, nullptr, LLM_NORM_RMS, -1);138    cb(cur, "result_norm", -1);139 140    res->t_embd = cur;141 142    cur = build_lora_mm(model.output, cur);143    cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);144    cb(cur, "result_output", -1);145 146    res->t_logits = cur;147 148    ggml_build_forward_expand(gf, cur);149}150