Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_seed_oss::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 64: type = LLM_TYPE_36B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_seed_oss::load_arch_tensors(llama_model_loader &) {13    LLAMA_LOAD_LOCALS;14 15    const uint32_t head_dim             = hparams.n_embd_head_k();16    const int64_t n_qo_dim              = n_head * head_dim;17    const int64_t n_kv_dim              = n_head_kv * head_dim;18 19    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);20 21    // output22    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}, TENSOR_NOT_REQUIRED);24    // if output is NULL, init from the input tok embed25    if (output == NULL) {26        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);27    }28 29    for (int i = 0; i < n_layer; ++i) {30        auto & layer = layers[i];31 32        create_tensor_qkv(layer, i, n_embd, n_qo_dim, n_kv_dim, n_kv_dim, 0);33        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, 0);34 35 36        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);37        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);38 39        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);40        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);41        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);42    }43}44 45std::unique_ptr<llm_graph_context> llama_model_seed_oss::build_arch_graph(const llm_graph_params & params) const {46    return std::make_unique<graph>(*this, params);47}48 49llama_model_seed_oss::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {50    const int64_t n_embd_head = hparams.n_embd_head_v();51 52    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());53    GGML_ASSERT(n_embd_head == n_rot);54 55    ggml_tensor * cur;56    ggml_tensor * inpL;57 58    inpL = build_inp_embd(model.tok_embd);59 60    // inp_pos - contains the positions61    ggml_tensor * inp_pos = build_inp_pos();62 63    auto * inp_attn = build_attn_inp_kv();64 65    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;66 67    ggml_tensor * inp_out_ids = build_inp_out_ids();68 69    for (int il = 0; il < n_layer; ++il) {70        ggml_tensor * inpSA = inpL;71 72        // norm73        cur = build_norm(inpL,74                model.layers[il].attn_norm, NULL,75                LLM_NORM_RMS, il);76        cb(cur, "attn_norm", il);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            Qcur = ggml_rope_ext(85                    ctx0, Qcur, 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 90            Kcur = ggml_rope_ext(91                    ctx0, Kcur, inp_pos, nullptr,92                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,93                    ext_factor, attn_factor, beta_fast, beta_slow94                    );95 96            cb(Qcur, "Qcur", il);97            cb(Kcur, "Kcur", il);98            cb(Vcur, "Vcur", il);99 100            cur = build_attn(inp_attn,101                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,102                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);103            cb(cur, "attn_out", il);104        }105        if (il == n_layer - 1 && inp_out_ids) {106            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);107            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);108        }109        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);110        cb(ffn_inp, "ffn_inp", il);111 112        // feed-forward network113        cur = build_norm(ffn_inp,114                model.layers[il].attn_post_norm, NULL,115                LLM_NORM_RMS, il);116        cb(cur, "attn_post_norm", il);117 118        cur = build_ffn(cur,119                model.layers[il].ffn_up,   NULL, NULL,120                model.layers[il].ffn_gate, NULL, NULL,121                model.layers[il].ffn_down, NULL, NULL,122                NULL,123                LLM_FFN_SILU, LLM_FFN_PAR, il);124        cb(cur, "ffn_out", il);125 126        cur = ggml_add(ctx0, cur, ffn_inp);127        cb(cur, "ffn_out", il);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