Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_bailingmoe::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);6    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);7    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);8    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);9    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);10 11    switch (hparams.n_layer()) {12        case 28: type = LLM_TYPE_16B; break;13        case 88: type = LLM_TYPE_290B; break;14        default: type = LLM_TYPE_UNKNOWN;15    }16}17 18void llama_model_bailingmoe::load_arch_tensors(llama_model_loader &) {19    LLAMA_LOAD_LOCALS;20    const int64_t n_expert_shared = hparams.n_expert_shared;21 22    const int64_t n_ff_exp            = hparams.n_ff_exp();23 24    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);25 26    // output27    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);28    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);29 30    for (int i = 0; i < n_layer; ++i) {31        auto & layer = layers[i];32 33        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);34 35        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);36        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);37        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);38 39        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);40 41        if (n_expert == 0) {42            throw std::runtime_error("n_expert must be > 0");43        }44        if (n_expert_used == 0) {45            throw std::runtime_error("n_expert_used must be > 0");46        }47 48        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);49        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);50        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);51 52        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);53        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);54        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);55    }56}57 58std::unique_ptr<llm_graph_context> llama_model_bailingmoe::build_arch_graph(const llm_graph_params & params) const {59    return std::make_unique<graph>(*this, params);60}61 62llama_model_bailingmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {63    ggml_tensor * cur;64    ggml_tensor * inpL;65 66    inpL = build_inp_embd(model.tok_embd);67 68    // inp_pos - contains the positions69    ggml_tensor * inp_pos = build_inp_pos();70 71    auto * inp_attn = build_attn_inp_kv();72 73    ggml_tensor * inp_out_ids = build_inp_out_ids();74 75    for (int il = 0; il < n_layer; ++il) {76        ggml_tensor * inpSA = inpL;77 78        // norm79        cur = build_norm(inpL,80                model.layers[il].attn_norm, NULL,81                LLM_NORM_RMS, il);82        cb(cur, "attn_norm", il);83 84        // self-attention85        {86            // rope freq factors for llama3; may return nullptr for llama2 and other models87            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);88 89            // compute Q and K and RoPE them90            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,91                    n_embd_head_k, n_head, n_head_kv, il);92 93            Qcur = ggml_rope_ext(94                    ctx0, Qcur, inp_pos, rope_factors,95                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,96                    ext_factor, attn_factor, beta_fast, beta_slow97                    );98 99            Kcur = ggml_rope_ext(100                    ctx0, Kcur, inp_pos, rope_factors,101                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,102                    ext_factor, attn_factor, beta_fast, beta_slow103                    );104 105            cb(Qcur, "Qcur", il);106            cb(Kcur, "Kcur", il);107            cb(Vcur, "Vcur", il);108 109            cur = build_attn(inp_attn,110                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,111                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il);112        }113 114        if (il == n_layer - 1 && inp_out_ids) {115            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);116            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);117        }118 119        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);120        cb(ffn_inp, "ffn_inp", il);121 122        cur = build_norm(ffn_inp,123                model.layers[il].ffn_norm, NULL,124                LLM_NORM_RMS, il);125        cb(cur, "ffn_norm", il);126 127        ggml_tensor * moe_out =128            build_moe_ffn(cur,129                    model.layers[il].ffn_gate_inp,130                    model.layers[il].ffn_up_exps,131                    model.layers[il].ffn_gate_exps,132                    model.layers[il].ffn_down_exps,133                    nullptr,134                    n_expert, n_expert_used,135                    LLM_FFN_SILU, hparams.expert_weights_norm,136                    hparams.expert_weights_scale,137                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,138                    il);139        cb(moe_out, "ffn_moe_out", il);140 141        // FFN shared expert142        {143            ggml_tensor * ffn_shexp = build_ffn(cur,144                    model.layers[il].ffn_up_shexp,   NULL, NULL,145                    model.layers[il].ffn_gate_shexp, NULL, NULL,146                    model.layers[il].ffn_down_shexp, NULL, NULL,147                    NULL,148                    LLM_FFN_SILU, LLM_FFN_PAR, il);149            cb(ffn_shexp, "ffn_shexp", il);150 151            cur = ggml_add(ctx0, moe_out, ffn_shexp);152            cb(cur, "ffn_out", il);153        }154 155        cur = ggml_add(ctx0, cur, ffn_inp);156 157        cur = build_cvec(cur, il);158        cb(cur, "l_out", il);159 160        // input for next layer161        inpL = cur;162    }163 164    cur = inpL;165 166    cur = build_norm(cur,167            model.output_norm, NULL,168            LLM_NORM_RMS, -1);169 170    cb(cur, "result_norm", -1);171    res->t_embd = cur;172 173    // lm_head174    cur = build_lora_mm(model.output, cur, model.output_s);175 176    cb(cur, "result_output", -1);177    res->t_logits = cur;178 179    ggml_build_forward_expand(gf, cur);180}181