Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_internlm2::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    switch (hparams.n_layer()) {7        case 32: type = LLM_TYPE_7B; break;8        case 48: type = LLM_TYPE_20B; break;9        default: type = LLM_TYPE_UNKNOWN;10    }11}12 13void llama_model_internlm2::load_arch_tensors(llama_model_loader &) {14    LLAMA_LOAD_LOCALS;15 16    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);17 18    // output19    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);20    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);21 22    for (int i = 0; i < n_layer; ++i) {23        auto & layer = layers[i];24 25        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);26        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);27        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);28 29        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);30        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);31        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);32        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);33        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);34    }35}36 37std::unique_ptr<llm_graph_context> llama_model_internlm2::build_arch_graph(const llm_graph_params & params) const {38    return std::make_unique<graph>(*this, params);39}40 41llama_model_internlm2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {42    const int64_t n_embd_head = hparams.n_embd_head_v();43 44    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());45    GGML_ASSERT(n_embd_head == n_rot);46 47    ggml_tensor * cur;48    ggml_tensor * inpL;49 50    inpL = build_inp_embd(model.tok_embd);51 52    // inp_pos - contains the positions53    ggml_tensor * inp_pos = build_inp_pos();54 55    auto * inp_attn = build_attn_inp_kv();56 57    ggml_tensor * inp_out_ids = build_inp_out_ids();58 59    for (int il = 0; il < n_layer; ++il) {60        ggml_tensor * inpSA = inpL;61 62        // norm63        cur = build_norm(inpL,64                model.layers[il].attn_norm, NULL,65                LLM_NORM_RMS, il);66        cb(cur, "attn_norm", il);67 68        // self-attention69        {70            // compute Q and K and RoPE them71            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,72                    n_embd_head, n_head, n_head_kv, il);73 74            Qcur = ggml_rope_ext(75                    ctx0, Qcur, inp_pos, nullptr,76                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,77                    ext_factor, attn_factor, beta_fast, beta_slow78                    );79 80            Kcur = ggml_rope_ext(81                    ctx0, Kcur, inp_pos, nullptr,82                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,83                    ext_factor, attn_factor, beta_fast, beta_slow84                    );85 86            cb(Qcur, "Qcur", il);87            cb(Kcur, "Kcur", il);88            cb(Vcur, "Vcur", il);89 90            cur = build_attn(inp_attn,91                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,92                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);93        }94        if (il == n_layer - 1 && inp_out_ids) {95            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);96            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);97        }98        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);99        cb(ffn_inp, "ffn_inp", il);100 101        // feed-forward network102        cur = build_norm(ffn_inp,103                model.layers[il].ffn_norm, NULL,104                LLM_NORM_RMS, il);105        cb(cur, "ffn_norm", il);106 107        cur = build_ffn(cur,108                model.layers[il].ffn_up,   NULL, NULL,109                model.layers[il].ffn_gate, NULL, NULL,110                model.layers[il].ffn_down, NULL, NULL,111                NULL,112                LLM_FFN_SILU, LLM_FFN_PAR, il);113        cb(cur, "ffn_out", il);114 115        cur = ggml_add(ctx0, cur, ffn_inp);116 117        cur = build_cvec(cur, il);118        cb(cur, "l_out", il);119 120        // input for next layer121        inpL = cur;122    }123    cur = inpL;124 125    cur = build_norm(cur,126            model.output_norm, NULL,127            LLM_NORM_RMS, -1);128 129    cb(cur, "result_norm", -1);130    res->t_embd = cur;131 132    // lm_head133    cur = build_lora_mm(model.output, cur, model.output_s);134 135    cb(cur, "result_output", -1);136    res->t_logits = cur;137 138    ggml_build_forward_expand(gf, cur);139}140