Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_llada_moe::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all, false);5    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);6 7    // diffusion language model uses non-causal attention8    hparams.causal_attn = false;9 10    switch (hparams.n_layer()) {11        case 16: type = LLM_TYPE_A1_7B; break;12        default: type = LLM_TYPE_UNKNOWN;13    }14}15 16void llama_model_llada_moe::load_arch_tensors(llama_model_loader &) {17    LLAMA_LOAD_LOCALS;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}, 0);24 25    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for llada-moe");26    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for llada-moe");27 28    for (int i = 0; i < n_layer; ++i) {29        auto & layer = layers[i];30 31        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);32 33        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);34        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);35        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);36        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);37 38        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);39 40        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);41 42        const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;43 44        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);45        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);46        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);47    }48}49 50std::unique_ptr<llm_graph_context> llama_model_llada_moe::build_arch_graph(const llm_graph_params & params) const {51    return std::make_unique<graph>(*this, params);52}53 54llama_model_llada_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {55    const int64_t n_embd_head = hparams.n_embd_head_v();56 57    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());58    GGML_ASSERT(n_embd_head == n_rot);59 60    ggml_tensor * cur;61    ggml_tensor * inpL;62 63    inpL = build_inp_embd(model.tok_embd);64 65    // inp_pos - contains the positions66    ggml_tensor * inp_pos = build_inp_pos();67 68    auto * inp_attn = build_attn_inp_no_cache();69 70    ggml_tensor * inp_out_ids = build_inp_out_ids();71 72    for (int il = 0; il < n_layer; ++il) {73        ggml_tensor * inpSA = inpL;74 75        // norm76        cur = build_norm(inpL,77                model.layers[il].attn_norm, NULL,78                LLM_NORM_RMS, il);79        cb(cur, "attn_norm", il);80 81        // self_attention82        {83            // compute Q and K and RoPE them84            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,85                    n_embd_head, n_head, n_head_kv, il);86 87            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);88            cb(Qcur, "Qcur_normed", il);89 90            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);91            cb(Kcur, "Kcur_normed", il);92 93            Qcur = ggml_rope_ext(94                    ctx0, Qcur, inp_pos, nullptr,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, nullptr,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, NULL, model.layers[il].wo_s,111                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);112        }113        if (il == n_layer - 1 && inp_out_ids) {114            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);115            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);116        }117        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);118        cb(ffn_inp, "ffn_inp", il);119 120        // MoE branch121        cur = build_norm(ffn_inp,122                model.layers[il].ffn_norm, NULL,123                LLM_NORM_RMS, il);124        cb(cur, "ffn_norm", il);125 126        cur = build_moe_ffn(cur,127                model.layers[il].ffn_gate_inp,128                model.layers[il].ffn_up_exps,129                model.layers[il].ffn_gate_exps,130                model.layers[il].ffn_down_exps,131                nullptr,132                n_expert, n_expert_used,133                LLM_FFN_SILU, false,134                hparams.expert_weights_scale,135                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,136                il);137        cb(cur, "ffn_moe_out", il);138 139        cur = ggml_add(ctx0, cur, ffn_inp);140 141        cur = build_cvec(cur, il);142        cb(cur, "l_out", il);143 144        // input for next layer145        inpL = cur;146    }147    cur = inpL;148 149    cur = build_norm(cur,150            model.output_norm, NULL,151            LLM_NORM_RMS, -1);152 153    cb(cur, "result_norm", -1);154    res->t_embd = cur;155 156    // lm_head157    cur = build_lora_mm(model.output, cur, model.output_s);158 159    cb(cur, "result_output", -1);160    res->t_logits = cur;161 162    ggml_build_forward_expand(gf, cur);163}164