Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_qwen2moe::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_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);6 7    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);8 9    switch (hparams.n_layer()) {10        case 24: type = LLM_TYPE_A2_7B; break;11        case 28: type = LLM_TYPE_57B_A14B; break;12        default: type = LLM_TYPE_UNKNOWN;13    }14}15 16void llama_model_qwen2moe::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    for (int i = 0; i < n_layer; ++i) {26        auto & layer = layers[i];27 28        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);29 30        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);31        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);32 33        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);34 35        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);36 37        if (n_expert == 0) {38            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");39        }40        if (n_expert_used == 0) {41            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");42        }43 44        // MoE branch45        const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;46 47        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);48        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);49        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);50 51        // Shared expert branch52        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;53 54        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);55        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);56        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);57        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);58    }59}60 61std::unique_ptr<llm_graph_context> llama_model_qwen2moe::build_arch_graph(const llm_graph_params & params) const {62    return std::make_unique<graph>(*this, params);63}64 65llama_model_qwen2moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {66    const int64_t n_embd_head = hparams.n_embd_head_v();67 68    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());69    GGML_ASSERT(n_embd_head == n_rot);70 71    ggml_tensor * cur;72    ggml_tensor * inpL;73 74    inpL = build_inp_embd(model.tok_embd);75 76    // inp_pos - contains the positions77    ggml_tensor * inp_pos = build_inp_pos();78 79    auto * inp_attn = build_attn_inp_kv();80 81    ggml_tensor * inp_out_ids = build_inp_out_ids();82 83    for (int il = 0; il < n_layer; ++il) {84        ggml_tensor * inpSA = inpL;85 86        // norm87        cur = build_norm(inpL,88                model.layers[il].attn_norm, NULL,89                LLM_NORM_RMS, il);90        cb(cur, "attn_norm", il);91 92        // self_attention93        {94            // compute Q and K and RoPE them95            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,96                    n_embd_head, n_head, n_head_kv, il);97 98            Qcur = ggml_rope_ext(99                    ctx0, Qcur, inp_pos, nullptr,100                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,101                    ext_factor, attn_factor, beta_fast, beta_slow102                    );103 104            Kcur = ggml_rope_ext(105                    ctx0, Kcur, inp_pos, nullptr,106                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,107                    ext_factor, attn_factor, beta_fast, beta_slow108                    );109 110            cb(Qcur, "Qcur", il);111            cb(Kcur, "Kcur", il);112            cb(Vcur, "Vcur", il);113 114            cur = build_attn(inp_attn,115                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,116                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);117        }118        if (il == n_layer - 1 && inp_out_ids) {119            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);120            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);121        }122        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);123        cb(ffn_inp, "ffn_inp", il);124 125        // MoE branch126        cur = build_norm(ffn_inp,127                model.layers[il].ffn_norm, NULL,128                LLM_NORM_RMS, il);129        cb(cur, "ffn_norm", il);130 131        ggml_tensor * moe_out =132            build_moe_ffn(cur,133                    model.layers[il].ffn_gate_inp,134                    model.layers[il].ffn_up_exps,135                    model.layers[il].ffn_gate_exps,136                    model.layers[il].ffn_down_exps,137                    nullptr,138                    n_expert, n_expert_used,139                    LLM_FFN_SILU, false,140                    hparams.expert_weights_scale,141                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,142                    il);143        cb(moe_out, "ffn_moe_out", il);144 145        // FFN shared expert146        {147            ggml_tensor * cur_gate_inp = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);148            cb(cur_gate_inp, "ffn_shexp_gate_inp", il);149 150            // sigmoid151            ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);152            cb(cur_gate, "ffn_shexp_gate", il);153 154            ggml_tensor * cur_ffn = build_ffn(cur,155                    model.layers[il].ffn_up_shexp,   NULL, NULL,156                    model.layers[il].ffn_gate_shexp, NULL, NULL,157                    model.layers[il].ffn_down_shexp, NULL, NULL,158                    NULL,159                    LLM_FFN_SILU, LLM_FFN_PAR, il);160            cb(cur_ffn, "ffn_shexp", il);161 162            ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);163            cb(ffn_shexp_out, "ffn_shexp_out", il);164 165            moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);166            cb(moe_out, "ffn_out", il);167 168            cur = moe_out;169        }170        cur = ggml_add(ctx0, cur, ffn_inp);171 172        cur = build_cvec(cur, il);173        cb(cur, "l_out", il);174 175        // input for next layer176        inpL = cur;177    }178    cur = inpL;179 180    cur = build_norm(cur,181            model.output_norm, NULL,182            LLM_NORM_RMS, -1);183 184    cb(cur, "result_norm", -1);185    res->t_embd = cur;186 187    // lm_head188    cur = build_lora_mm(model.output, cur, model.output_s);189 190    cb(cur, "result_output", -1);191    res->t_logits = cur;192 193    ggml_build_forward_expand(gf, cur);194}195