Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_maple::load_arch_hparams(llama_model_loader & ml) {4    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;5 6    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);7    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);8    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);9 10    ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);11 12    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;13    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;14    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);15 16    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all);17 18    switch (hparams.n_layer()) {19        case 24: type = LLM_TYPE_20B; break;20        default: type = LLM_TYPE_UNKNOWN;21    }22}23 24void llama_model_maple::load_arch_tensors(llama_model_loader &) {25    LLAMA_LOAD_LOCALS;26 27    const int64_t n_ff_exp = hparams.n_ff_exp();28    const int64_t head_dim = hparams.n_embd_head_k();29 30    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);31 32    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);33    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);34 35    if (n_expert == 0) {36        throw std::runtime_error("n_expert must be > 0 for Maple");37    }38    if (n_expert_used == 0) {39        throw std::runtime_error("n_expert_used must be > 0 for Maple");40    }41 42    for (int i = 0; i < n_layer; ++i) {43        auto & layer = layers[i];44 45        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);46 47        create_tensor_qkv(layer, i, n_embd, n_head * head_dim, n_head_kv * head_dim, n_head_kv * head_dim, 0);48        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * head_dim, n_embd}, 0);49 50        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim}, 0);51        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim}, 0);52        layer.ffn_norm    = create_tensor(tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, 0);53 54        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);55        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);56        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);57        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);58    }59}60 61std::unique_ptr<llm_graph_context> llama_model_maple::build_arch_graph(const llm_graph_params & params) const {62    return std::make_unique<graph>(*this, params);63}64 65llama_model_maple::graph::graph(const llama_model & model, const llm_graph_params & params) :66    llm_graph_context(params) {67    const int64_t n_embd_head = hparams.n_embd_head_k();68 69    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());70 71    ggml_tensor * inpL = build_inp_embd(model.tok_embd);72    ggml_tensor * inp_pos = build_inp_pos();73    auto * inp_attn = build_attn_inp_kv_iswa();74    ggml_tensor * inp_out_ids = build_inp_out_ids();75 76    for (int il = 0; il < n_layer; ++il) {77        ggml_tensor * inpSA = inpL;78 79        ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);80        cb(cur, "attn_norm", il);81 82        {83            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);84 85            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);86            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);87            cb(Qcur, "Qcur_normed", il);88            cb(Kcur, "Kcur_normed", il);89 90            if (hparams.is_swa(il)) {91                const int64_t n_rot_l = hparams.n_rot(il);92                const float freq_base_l = model.get_rope_freq_base(cparams, il);93                const float freq_scale_l = model.get_rope_freq_scale(cparams, il);94 95                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l,96                                     freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow);97                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l,98                                     freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow);99            }100            cb(Qcur, "Qcur", il);101            cb(Kcur, "Kcur", il);102            cb(Vcur, "Vcur", il);103 104            cur = build_attn(inp_attn,105                    model.layers[il].wo, nullptr, model.layers[il].wo_s,106                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);107            cb(cur, "attn_out", il);108        }109 110        if (il == n_layer - 1 && inp_out_ids) {111            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);112            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);113        }114 115        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);116        cb(ffn_inp, "ffn_inp", il);117 118        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);119        cb(cur, "ffn_norm", il);120 121        cur = build_moe_ffn(cur,122                model.layers[il].ffn_gate_inp,123                model.layers[il].ffn_up_exps,124                model.layers[il].ffn_gate_exps,125                model.layers[il].ffn_down_exps,126                nullptr,127                n_expert, n_expert_used,128                LLM_FFN_SILU, true,129                1.0f,130                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,131                il);132        cb(cur, "ffn_moe_out", il);133 134        cur = ggml_add(ctx0, cur, ffn_inp);135        cur = build_cvec(cur, il);136        cb(cur, "l_out", il);137 138        inpL = cur;139    }140 141    ggml_tensor * cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1);142    cb(cur, "result_norm", -1);143    res->t_embd = cur;144 145    cur = build_lora_mm(model.output, cur, model.output_s);146    cb(cur, "result_output", -1);147    res->t_logits = cur;148 149    ggml_build_forward_expand(gf, cur);150}151