Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_hunyuan_moe::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_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);6    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);7 8    switch (hparams.n_layer()) {9        case 32: type = LLM_TYPE_A13B; break;10        default: type = LLM_TYPE_UNKNOWN;11    }12}13 14void llama_model_hunyuan_moe::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        const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(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_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);34        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);35 36        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);37        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);38 39        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);40 41        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);42        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, 0);43        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);44        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);45 46        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);47        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);48        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);49    }50}51 52std::unique_ptr<llm_graph_context> llama_model_hunyuan_moe::build_arch_graph(const llm_graph_params & params) const {53    return std::make_unique<graph>(*this, params);54}55 56llama_model_hunyuan_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {57    const int64_t n_embd_head = hparams.n_embd_head_v();58 59    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());60    GGML_ASSERT(n_embd_head == n_rot);61 62    ggml_tensor * cur;63    ggml_tensor * inpL;64 65    inpL = build_inp_embd(model.tok_embd);66 67    // inp_pos - contains the positions68    ggml_tensor * inp_pos = build_inp_pos();69 70    auto * inp_attn = build_attn_inp_kv();71 72    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));73 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        // norm80        cur = build_norm(inpL,81                model.layers[il].attn_norm, NULL,82                LLM_NORM_RMS, il);83        cb(cur, "attn_norm", il);84 85        // self-attention86        {87            // rope freq factors for llama3; may return nullptr for llama2 and other models88            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);89 90            // compute Q and K and RoPE them91            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,92                    n_embd_head, n_head, n_head_kv, il);93 94            Qcur = ggml_rope_ext(95                    ctx0, Qcur, inp_pos, rope_factors,96                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,97                    ext_factor, attn_factor, beta_fast, beta_slow98                    );99 100            cb(Qcur, "Qcur", il);101            cb(Kcur, "Kcur", il);102            cb(Vcur, "Vcur", il);103 104            Kcur = ggml_rope_ext(105                    ctx0, Kcur, inp_pos, rope_factors,106                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,107                    ext_factor, attn_factor, beta_fast, beta_slow108                    );109 110            Kcur = build_norm(Kcur,111                    model.layers[il].attn_k_norm, nullptr,112                    LLM_NORM_RMS, il);113            cb(Kcur, "Kcur_norm", il);114 115            Qcur = build_norm(Qcur,116                    model.layers[il].attn_q_norm, nullptr,117                    LLM_NORM_RMS, il);118            cb(Qcur, "Qcur_norm", il);119 120            cur = build_attn(inp_attn,121                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,122                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);123            cb(cur, "attn_out", il);124        }125        if (il == n_layer - 1 && inp_out_ids) {126            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);127            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);128        }129        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);130        cb(ffn_inp, "ffn_inp", il);131 132        cur = build_norm(ffn_inp,133            model.layers[il].ffn_norm, NULL,134            LLM_NORM_RMS, il);135        cb(cur, "ffn_norm", il);136 137        // feed-forward network (non-MoE)138        ggml_tensor * cur_mlp = build_ffn(cur,139                model.layers[il].ffn_up_shexp,   NULL, NULL,140                model.layers[il].ffn_gate_shexp, NULL, NULL,141                model.layers[il].ffn_down_shexp, NULL, NULL,142                NULL,143                LLM_FFN_SILU, LLM_FFN_PAR, il);144        cb(cur_mlp, "ffn_mlp", il);145 146        // MoE branch147        ggml_tensor * cur_moe = build_moe_ffn(cur,148                model.layers[il].ffn_gate_inp,149                model.layers[il].ffn_up_exps,150                model.layers[il].ffn_gate_exps,151                model.layers[il].ffn_down_exps,152                nullptr,153                n_expert, n_expert_used,154                LLM_FFN_SILU,155                true, // norm_topk_prob156                hparams.expert_weights_scale,157                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,158                il);159        cb(cur_moe, "ffn_moe_out", il);160 161        ggml_tensor * ffn_out = ggml_add(ctx0, cur_moe, cur_mlp);162        cb(ffn_out, "ffn_out", il);163 164        cur = ggml_add(ctx0, ffn_out, ffn_inp);165 166        cur = build_cvec(cur, il);167        cb(cur, "l_out", il);168 169        // input for next layer170        inpL = cur;171    }172    cur = inpL;173 174    cur = build_norm(cur,175            model.output_norm, NULL,176            LLM_NORM_RMS, -1);177 178    cb(cur, "result_norm", -1);179    res->t_embd = cur;180 181    // lm_head182    cur = build_lora_mm(model.output, cur, model.output_s);183    cb(cur, "result_output", -1);184    res->t_logits = cur;185 186    ggml_build_forward_expand(gf, cur);187}188