Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_bitnet::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 26: type = LLM_TYPE_3B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_bitnet::load_arch_tensors(llama_model_loader &) {13    LLAMA_LOAD_LOCALS;14 15    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);16 17    // output18    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);19 20    for (int i = 0; i < n_layer; ++i) {21        auto & layer = layers[i];22 23        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);24        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);25 26        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);27        layer.wq_s     = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);28        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);29        layer.wk_s     = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);30        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);31        layer.wv_s     = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);32        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);33        layer.wo_s     = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, TENSOR_NOT_REQUIRED);34 35        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);36        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);37 38        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);39        layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, TENSOR_NOT_REQUIRED);40        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);41        layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, TENSOR_NOT_REQUIRED);42        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);43        layer.ffn_up_s   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);44    }45}46 47std::unique_ptr<llm_graph_context> llama_model_bitnet::build_arch_graph(const llm_graph_params & params) const {48    return std::make_unique<graph>(*this, params);49}50 51llama_model_bitnet::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {52    const int64_t n_embd_head = hparams.n_embd_head_v();53 54    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());55 56    ggml_tensor * cur;57    ggml_tensor * inpL;58 59    inpL = build_inp_embd(model.tok_embd);60 61    // inp_pos - contains the positions62    ggml_tensor * inp_pos = build_inp_pos();63 64    auto * inp_attn = build_attn_inp_kv();65 66    ggml_tensor * inp_out_ids = build_inp_out_ids();67 68    for (int il = 0; il < n_layer; ++il) {69        ggml_tensor * inpSA = inpL;70 71        cur = build_norm(inpL,72                model.layers[il].attn_norm, NULL,73                LLM_NORM_RMS, il);74        cb(cur, "attn_norm", il);75 76        // self-attention77        {78            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,79                    n_embd_head, n_head, n_head_kv, il);80 81            Qcur = ggml_rope_ext(82                    ctx0, Qcur, inp_pos, nullptr,83                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,84                    ext_factor, attn_factor, beta_fast, beta_slow85                    );86 87            Kcur = ggml_rope_ext(88                    ctx0, Kcur, inp_pos, nullptr,89                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,90                    ext_factor, attn_factor, beta_fast, beta_slow91                    );92 93            cb(Qcur, "Qcur", il);94            cb(Kcur, "Kcur", il);95            cb(Vcur, "Vcur", il);96 97            cur = build_attn(inp_attn,98                    NULL, NULL, NULL,99                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);100 101            cur = build_norm(cur,102                    model.layers[il].attn_sub_norm, NULL,103                    LLM_NORM_RMS, il);104            cb(cur, "attn_sub_norm", il);105 106            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);107            if (model.layers[il].wo_b) {108                cur = ggml_add(ctx0, cur, model.layers[il].wo_b);109            }110            cb(cur, "attn_out", il);111        }112 113        if (il == n_layer - 1 && inp_out_ids) {114            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);115            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);116        }117 118        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);119        cb(ffn_inp, "ffn_inp", il);120 121        // feed-forward forward122        cur = build_norm(ffn_inp,123                model.layers[il].ffn_norm, NULL,124                LLM_NORM_RMS, il);125        cb(cur, "ffn_norm", il);126 127        cur = build_ffn(cur,128                model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_s,129                model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,130                NULL,                      NULL, NULL,131                NULL,132                LLM_FFN_SILU, LLM_FFN_PAR, il);133        cb(cur, "ffn_sub_out", il);134 135        cur = build_norm(cur,136                model.layers[il].ffn_sub_norm, NULL,137                LLM_NORM_RMS, il);138        cb(cur, "ffn_sub_norm", il);139 140        cur = build_lora_mm(model.layers[il].ffn_down, cur, model.layers[il].ffn_down_s);141        cb(cur, "ffn_down", il);142 143        cur = ggml_add(ctx0, cur, ffn_inp);144        cb(cur, "l_out", il);145 146        cur = build_cvec(cur, il);147        cb(cur, "l_out", il);148 149        // input for next layer150        inpL = cur;151    }152 153    cur = inpL;154 155    cur = build_norm(cur,156            model.output_norm, NULL,157            LLM_NORM_RMS, -1);158 159    cb(cur, "result_norm", -1);160    res->t_embd = cur;161 162    // lm_head163    // FIXME: do not use model.tok_embd directly, duplicate as model.output164    cur = build_lora_mm(model.tok_embd, cur);165 166    cb(cur, "result_output", -1);167    res->t_logits = cur;168 169    ggml_build_forward_expand(gf, cur);170}171