Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_baichuan::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    switch (hparams.n_layer()) {6        case 32: type = LLM_TYPE_7B; break;7        case 40: type = LLM_TYPE_13B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10 11    if (type == LLM_TYPE_13B) {12        // TODO: become GGUF KV parameter13        hparams.f_max_alibi_bias = 8.0f;14    }15}16 17void llama_model_baichuan::load_arch_tensors(llama_model_loader &) {18    LLAMA_LOAD_LOCALS;19 20    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);21    {22        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);23        output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);24    }25 26    for (int i = 0; i < n_layer; ++i) {27        auto & layer = layers[i];28 29        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);30 31        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);32        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);33 34        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);35 36        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);37        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);38        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);39    }40}41 42std::unique_ptr<llm_graph_context> llama_model_baichuan::build_arch_graph(const llm_graph_params & params) const {43    return std::make_unique<graph>(*this, params);44}45 46llama_model_baichuan::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {47    const int64_t n_embd_head = hparams.n_embd_head_v();48 49    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());50    GGML_ASSERT(n_embd_head == n_rot);51 52    ggml_tensor * cur;53    ggml_tensor * inpL;54 55    inpL = build_inp_embd(model.tok_embd);56 57    // inp_pos - contains the positions58    ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;59 60    auto * inp_attn = build_attn_inp_kv();61 62    ggml_tensor * inp_out_ids = build_inp_out_ids();63 64    for (int il = 0; il < n_layer; ++il) {65        ggml_tensor * inpSA = inpL;66 67        cur = build_norm(inpL,68                model.layers[il].attn_norm, NULL,69                LLM_NORM_RMS, il);70        cb(cur, "attn_norm", il);71 72        // self-attention73        {74            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,75                    n_embd_head, n_head, n_head_kv, il);76 77            switch (model.type) {78                case LLM_TYPE_7B:79                    Qcur = ggml_rope_ext(80                            ctx0, Qcur, inp_pos, nullptr,81                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,82                            ext_factor, attn_factor, beta_fast, beta_slow83                            );84                    Kcur = ggml_rope_ext(85                            ctx0, Kcur, inp_pos, nullptr,86                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,87                            ext_factor, attn_factor, beta_fast, beta_slow88                            );89                    break;90                case LLM_TYPE_13B:91                case LLM_TYPE_UNKNOWN:92                    break;93                default:94                    GGML_ABORT("fatal error");95            }96 97            cb(Qcur, "Qcur", il);98            cb(Kcur, "Kcur", il);99            cb(Vcur, "Vcur", il);100 101            cur = build_attn(inp_attn,102                    model.layers[il].wo, NULL, model.layers[il].wo_s,103                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);104        }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        }110 111        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);112        cb(ffn_inp, "ffn_inp", il);113 114        // feed-forward network115        {116            cur = build_norm(ffn_inp,117                    model.layers[il].ffn_norm, NULL,118                    LLM_NORM_RMS, il);119            cb(cur, "ffn_norm", il);120 121            cur = build_ffn(cur,122                    model.layers[il].ffn_up,   NULL, NULL,123                    model.layers[il].ffn_gate, NULL, NULL,124                    model.layers[il].ffn_down, NULL, NULL,125                    NULL,126                    LLM_FFN_SILU, LLM_FFN_PAR, il);127            cb(cur, "ffn_out", il);128        }129 130        cur = ggml_add(ctx0, cur, ffn_inp);131 132        cur = build_cvec(cur, il);133        cb(cur, "l_out", il);134 135        // input for next layer136        inpL = cur;137    }138 139    cur = inpL;140 141    cur = build_norm(cur,142            model.output_norm, NULL,143            LLM_NORM_RMS, -1);144 145    cb(cur, "result_norm", -1);146    res->t_embd = cur;147 148    // lm_head149    cur = build_lora_mm(model.output, cur, model.output_s);150 151    cb(cur, "result_output", -1);152    res->t_logits = cur;153 154    ggml_build_forward_expand(gf, cur);155}156