Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_qwen3moe::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    switch (hparams.n_layer()) {8        case 48: type = LLM_TYPE_30B_A3B; break;9        case 94: type = LLM_TYPE_235B_A22B; break;10        default: type = LLM_TYPE_UNKNOWN;11    }12}13 14void llama_model_qwen3moe::load_arch_tensors(llama_model_loader &) {15    LLAMA_LOAD_LOCALS;16 17    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);18 19    // output20    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);21    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);22    // if output is NULL, init from the input tok embed23    if (output == NULL) {24        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);25    }26 27    for (int i = 0; i < n_layer; ++i) {28        auto & layer = layers[i];29 30        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);31 32        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);33        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);34 35        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);36        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_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        if (n_expert == 0) {43            throw std::runtime_error("n_expert must be > 0 for QWEN3MOE");44        }45        if (n_expert_used == 0) {46            throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");47        }48 49        // MoE branch50        const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;51 52        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);53        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);54        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);55    }56}57 58std::unique_ptr<llm_graph_context> llama_model_qwen3moe::build_arch_graph(const llm_graph_params & params) const {59    return std::make_unique<graph>(*this, params);60}61 62llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {63    const int64_t n_embd_head = hparams.n_embd_head_v();64 65    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());66    GGML_ASSERT(n_embd_head == n_rot);67 68    ggml_tensor * cur;69    ggml_tensor * inpL;70 71    inpL = build_inp_embd(model.tok_embd);72 73    // inp_pos - contains the positions74    ggml_tensor * inp_pos = build_inp_pos();75 76    auto * inp_attn = build_attn_inp_kv();77 78    ggml_tensor * inp_out_ids = build_inp_out_ids();79 80    for (int il = 0; il < n_layer; ++il) {81        res->t_layer_inp[il] = inpL;82 83        ggml_tensor * inpSA = inpL;84 85        // norm86        cur = build_norm(inpL,87                model.layers[il].attn_norm, NULL,88                LLM_NORM_RMS, il);89        cb(cur, "attn_norm", il);90 91        // self_attention92        {93            // compute Q and K and RoPE them94            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,95                    n_embd_head, n_head, n_head_kv, il);96 97            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);98            cb(Qcur, "Qcur_normed", il);99 100            Qcur = ggml_rope_ext(101                    ctx0, Qcur, inp_pos, nullptr,102                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,103                    ext_factor, attn_factor, beta_fast, beta_slow104                    );105 106            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);107            cb(Kcur, "Kcur_normed", il);108 109            Kcur = ggml_rope_ext(110                    ctx0, Kcur, inp_pos, nullptr,111                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,112                    ext_factor, attn_factor, beta_fast, beta_slow113                    );114 115            cb(Qcur, "Qcur", il);116            cb(Kcur, "Kcur", il);117            cb(Vcur, "Vcur", il);118 119            cur = build_attn(inp_attn,120                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,121                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);122        }123        if (il == n_layer - 1 && inp_out_ids) {124            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);125            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);126        }127        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);128        cb(ffn_inp, "ffn_inp", il);129 130        // MoE branch131        cur = build_norm(ffn_inp,132                model.layers[il].ffn_norm, NULL,133                LLM_NORM_RMS, il);134        cb(cur, "ffn_norm", il);135 136        ggml_tensor * moe_out =137            build_moe_ffn(cur,138                    model.layers[il].ffn_gate_inp,139                    model.layers[il].ffn_up_exps,140                    model.layers[il].ffn_gate_exps,141                    model.layers[il].ffn_down_exps,142                    nullptr,143                    n_expert, n_expert_used,144                    LLM_FFN_SILU, true,145                    hparams.expert_weights_scale,146                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,147                    il,148                    nullptr, nullptr,149                    model.layers[il].ffn_up_exps_s,150                    model.layers[il].ffn_gate_exps_s,151                    model.layers[il].ffn_down_exps_s);152        cb(moe_out, "ffn_moe_out", il);153        cur = moe_out;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    cur = inpL;164 165    cur = build_norm(cur,166            model.output_norm, NULL,167            LLM_NORM_RMS, -1);168 169    cb(cur, "result_norm", -1);170    res->t_embd = cur;171 172    // lm_head173    cur = build_lora_mm(model.output, cur, model.output_s);174 175    cb(cur, "result_output", -1);176    res->t_logits = cur;177 178    ggml_build_forward_expand(gf, cur);179}180