Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_stablelm::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);5 6    switch (hparams.n_layer()) {7        case 24: type = LLM_TYPE_1B; break;8        case 32: type = LLM_TYPE_3B; break;9        case 40: type = LLM_TYPE_12B; break;10        default: type = LLM_TYPE_UNKNOWN;11   }12}13 14void llama_model_stablelm::load_arch_tensors(llama_model_loader &) {15    LLAMA_LOAD_LOCALS;16 17    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);18 19    // output20    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);21    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);22    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);23 24    for (int i = 0; i < n_layer; ++i) {25        auto & layer = layers[i];26 27        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);28        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);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        // optional q and k layernorms, present in StableLM 2 12B34        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    TENSOR_NOT_REQUIRED);35        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);36 37        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual38        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);39        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);40 41        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);42        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);43        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);44    }45}46 47std::unique_ptr<llm_graph_context> llama_model_stablelm::build_arch_graph(const llm_graph_params & params) const {48    return std::make_unique<graph>(*this, params);49}50 51llama_model_stablelm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {52    const int64_t n_embd_head = hparams.n_embd_head_v();53 54    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());55 56    ggml_tensor * cur;57    ggml_tensor * inpL;58 59    inpL = build_inp_embd(model.tok_embd);60 61    // inp_pos - contains the positions62    ggml_tensor * inp_pos = build_inp_pos();63 64    auto * inp_attn = build_attn_inp_kv();65 66    ggml_tensor * inp_out_ids = build_inp_out_ids();67 68    for (int il = 0; il < n_layer; ++il) {69        // norm70        cur = build_norm(inpL,71                model.layers[il].attn_norm,72                model.layers[il].attn_norm_b,73                LLM_NORM, il);74        cb(cur, "attn_norm", il);75 76        ggml_tensor * inpSA = cur;77 78        // self-attention79        {80            // compute Q and K and RoPE them81            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,82                    n_embd_head, n_head, n_head_kv, il);83 84            if (model.layers[il].attn_q_norm) {85                Qcur = build_norm(Qcur,86                        model.layers[il].attn_q_norm,87                        NULL,88                        LLM_NORM, il);89                cb(Qcur, "Qcur", il);90            }91            if (model.layers[il].attn_k_norm) {92                Kcur = build_norm(Kcur,93                        model.layers[il].attn_k_norm,94                        NULL,95                        LLM_NORM, il);96                cb(Kcur, "Kcur", il);97            }98 99            Qcur = ggml_rope_ext(100                    ctx0, Qcur, inp_pos, nullptr,101                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,102                    ext_factor, attn_factor, beta_fast, beta_slow103                    );104 105            Kcur = ggml_rope_ext(106                    ctx0, Kcur, inp_pos, nullptr,107                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,108                    ext_factor, attn_factor, beta_fast, beta_slow109                    );110 111            cb(Qcur, "Qcur", il);112            cb(Kcur, "Kcur", il);113            cb(Vcur, "Vcur", il);114 115            cur = build_attn(inp_attn,116                    model.layers[il].wo, NULL, model.layers[il].wo_s,117                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);118        }119        if (il == n_layer - 1 && inp_out_ids) {120            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);121            inpL  = ggml_get_rows(ctx0,  inpL, inp_out_ids);122            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);123        }124        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);125        cb(ffn_inp, "ffn_inp", il);126 127        // feed-forward network128        {129            if (model.layers[il].ffn_norm) {130                cur = build_norm(ffn_inp,131                        model.layers[il].ffn_norm,132                        model.layers[il].ffn_norm_b,133                        LLM_NORM, il);134                cb(cur, "ffn_norm", il);135            } else {136                // parallel residual137                cur = inpSA;138            }139            cur = build_ffn(cur,140                    model.layers[il].ffn_up,   NULL, NULL,141                    model.layers[il].ffn_gate, NULL, NULL,142                    model.layers[il].ffn_down, NULL, NULL,143                    NULL,144                    LLM_FFN_SILU, LLM_FFN_PAR, il);145            cb(cur, "ffn_out", il);146        }147        cur = ggml_add(ctx0, cur, ffn_inp);148 149        cur = build_cvec(cur, il);150        cb(cur, "l_out", il);151 152        // input for next layer153        inpL = cur;154    }155    cur = inpL;156 157    cur = build_norm(cur,158            model.output_norm,159            model.output_norm_b,160            LLM_NORM, -1);161 162    cb(cur, "result_norm", -1);163    res->t_embd = cur;164 165    // lm_head166    cur = build_lora_mm(model.output, cur, model.output_s);167 168    cb(cur, "result_output", -1);169    res->t_logits = cur;170 171    ggml_build_forward_expand(gf, cur);172}173