Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_arctic::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    if (hparams.n_expert == 128) {7        switch (hparams.n_layer()) {8            case 35: type = LLM_TYPE_10B_128x3_66B; break;9            default: type = LLM_TYPE_UNKNOWN;10        }11    } else {12        type = LLM_TYPE_UNKNOWN;13    }14}15 16void llama_model_arctic::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}, TENSOR_NOT_REQUIRED);24 25    // if output is NULL, init from the input tok embed26    if (output == NULL) {27        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);28    }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_embd, n_embd_gqa, n_embd_gqa, 0);36        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);37 38        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);39 40        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);41        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);42        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);43 44        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);45        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);46        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);47        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);48        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);49    }50}51 52std::unique_ptr<llm_graph_context> llama_model_arctic::build_arch_graph(const llm_graph_params & params) const {53    return std::make_unique<graph>(*this, params);54}55 56llama_model_arctic::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    ggml_tensor * inp_out_ids = build_inp_out_ids();73 74    for (int il = 0; il < n_layer; ++il) {75        ggml_tensor * inpSA = inpL;76 77        // norm78        cur = build_norm(inpL,79                model.layers[il].attn_norm, NULL,80                LLM_NORM_RMS, il);81        cb(cur, "attn_norm", il);82 83        // self-attention84        {85            // compute Q and K and RoPE them86            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,87                    n_embd_head, n_head, n_head_kv, il);88 89            Qcur = ggml_rope_ext(90                    ctx0, Qcur, inp_pos, nullptr,91                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,92                    ext_factor, attn_factor, beta_fast, beta_slow93                    );94 95            Kcur = ggml_rope_ext(96                    ctx0, Kcur, inp_pos, nullptr,97                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,98                    ext_factor, attn_factor, beta_fast, beta_slow99                    );100 101            cb(Qcur, "Qcur", il);102            cb(Kcur, "Kcur", il);103            cb(Vcur, "Vcur", il);104 105            cur = build_attn(inp_attn,106                    model.layers[il].wo, NULL, model.layers[il].wo_s,107                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);108        }109 110        if (il == n_layer - 1 && inp_out_ids) {111            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);112            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);113        }114 115        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);116        cb(ffn_inp, "ffn_inp", il);117 118        // feed-forward network119        cur = build_norm(ffn_inp,120                model.layers[il].ffn_norm, NULL,121                LLM_NORM_RMS, il);122        cb(cur, "ffn_norm", il);123 124        cur = build_ffn(cur,125                model.layers[il].ffn_up,   NULL, NULL,126                model.layers[il].ffn_gate, NULL, NULL,127                model.layers[il].ffn_down, NULL, NULL,128                NULL,129                LLM_FFN_SILU, LLM_FFN_PAR, il);130        cb(cur, "ffn_out", il);131 132        ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);133        cb(ffn_out, "ffn_out", il);134 135        // MoE136        cur = build_norm(inpSA,137                model.layers[il].ffn_norm_exps, NULL,138                LLM_NORM_RMS, il);139        cb(cur, "ffn_norm_exps", il);140 141        cur = build_moe_ffn(cur,142                model.layers[il].ffn_gate_inp,143                model.layers[il].ffn_up_exps,144                model.layers[il].ffn_gate_exps,145                model.layers[il].ffn_down_exps,146                nullptr,147                n_expert, n_expert_used,148                LLM_FFN_SILU, true,149                hparams.expert_weights_scale,150                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,151                il);152        cb(cur, "ffn_moe_out", il);153 154        cur = ggml_add(ctx0, cur, ffn_out);155        cb(cur, "ffn_out", il);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