Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    uint32_t n_loops_u = 1;7    ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false);8    GGML_ASSERT(n_loops_u >= 1);9 10    skip_loop_final_norm = false;11    ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false);12 13    n_layer_phys = (int) hparams.n_layer();14 15    // Bound-check before casting: signed int mul can overflow and bypass the guard.16    GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS);17    n_loops = (int) n_loops_u;18 19    // Expand logical layer count before load_tensors() allocates layers / KV.20    if (n_loops > 1) {21        for (int j = 1; j < n_loops; ++j) {22            for (int i = 0; i < n_layer_phys; ++i) {23                const int dst = i + j * n_layer_phys;24                hparams.n_head_arr[dst]    = hparams.n_head_arr[i];25                hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i];26                hparams.n_ff_arr[dst]      = hparams.n_ff_arr[i];27                hparams.is_swa_impl[dst]   = hparams.is_swa_impl[i];28                hparams.is_recr_impl[dst]  = hparams.is_recr_impl[i];29            }30        }31        hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops);32    }33 34    type = LLM_TYPE_UNKNOWN;35}36 37void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) {38    LLAMA_LOAD_LOCALS;39 40    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);41 42    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);43    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);44    if (output == NULL) {45        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);46    }47 48    const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer;49    for (int i = 0; i < n_phys; ++i) {50        auto & layer = layers[i];51 52        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);53 54        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);55        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);56 57        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},58                TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));59 60        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);61        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);62        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);63        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);64    }65 66    // Share physical weights across loops; each slot still has its own KV index.67    if (n_loops > 1) {68        for (int j = 1; j < n_loops; ++j) {69            for (int i = 0; i < n_phys; ++i) {70                layers[i + j * n_phys] = layers[i];71            }72        }73    }74}75 76std::unique_ptr<llm_graph_context> llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const {77    return std::make_unique<graph>(*this, params);78}79 80llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) :81        llm_graph_context(params) {82    const auto & nb = static_cast<const llama_model_nanbeige &>(model);83 84    const int64_t n_embd_head = hparams.n_embd_head_v();85    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());86 87    const int n_phys  = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer;88    const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1;89 90    ggml_tensor * cur;91    ggml_tensor * inpL;92 93    inpL = build_inp_embd(model.tok_embd);94 95    ggml_tensor * inp_pos = build_inp_pos();96 97    auto * inp_attn = build_attn_inp_kv();98 99    const float kq_scale = hparams.f_attention_scale == 0.0f100        ? 1.0f / sqrtf(float(n_embd_head))101        : hparams.f_attention_scale;102 103    ggml_tensor * inp_out_ids = build_inp_out_ids();104 105    for (int il = 0; il < n_layer; ++il) {106        res->t_layer_inp[il] = inpL;107        ggml_tensor * inpSA = inpL;108 109        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);110        cb(cur, "attn_norm", il);111 112        {113            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);114 115            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,116                    n_embd_head, n_head, n_head_kv, il);117 118            Qcur = ggml_rope_ext(119                    ctx0, Qcur, inp_pos, rope_factors,120                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,121                    ext_factor, attn_factor, beta_fast, beta_slow);122 123            Kcur = ggml_rope_ext(124                    ctx0, Kcur, inp_pos, rope_factors,125                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,126                    ext_factor, attn_factor, beta_fast, beta_slow);127 128            cb(Qcur, "Qcur", il);129            cb(Kcur, "Kcur", il);130            cb(Vcur, "Vcur", il);131 132            cur = build_attn(inp_attn,133                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,134                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);135            cb(cur, "attn_out", il);136        }137 138        if (il == n_layer - 1 && inp_out_ids) {139            cur   = ggml_get_rows(ctx0, cur,   inp_out_ids);140            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);141        }142 143        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);144        cb(ffn_inp, "ffn_inp", il);145 146        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);147        cb(cur, "ffn_norm", il);148 149        cur = build_ffn(cur,150                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   model.layers[il].ffn_up_s,151                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,152                model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,153                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);154        cb(cur, "ffn_out", il);155 156        cur = ggml_add(ctx0, cur, ffn_inp);157        cb(cur, "ffn_out", il);158 159        cur = build_cvec(cur, il);160        cb(cur, "l_out", il);161 162        inpL = cur;163 164        if (n_loops > 1 &&165            ((il + 1) % n_phys) == 0 &&166            (il + 1) < n_layer &&167            !nb.skip_loop_final_norm) {168            cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il);169            cb(cur, "loop_norm", il);170            inpL = cur;171        }172    }173 174    cur = inpL;175 176    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);177    cb(cur, "result_norm", -1);178    res->t_embd = cur;179 180    cur = build_lora_mm(model.output, cur, model.output_s);181    cb(cur, "result_output", -1);182    res->t_logits = cur;183 184    ggml_build_forward_expand(gf, cur);185}186