Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_chatglm::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 28: {8            if (hparams.n_head(0) == 16) {9                type = LLM_TYPE_1_5B;10            } else {11                type = LLM_TYPE_6B;12            }13        } break;14        case 40: {15            if (hparams.n_head(0) == 24) {16                type = LLM_TYPE_4B;17            } else {18                type = LLM_TYPE_9B;19            }20        } break;21        default: type = LLM_TYPE_UNKNOWN;22    }23}24 25void llama_model_chatglm::load_arch_tensors(llama_model_loader &) {26    LLAMA_LOAD_LOCALS;27 28    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);29 30    // output31    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);32    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);33    // if output is NULL, init from the input tok embed34    if (output == NULL) {35        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);36    }37 38    for (int i = 0; i < n_layer; ++i) {39        auto & layer = layers[i];40 41        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);42        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);43 44        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);45 46        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);47 48        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);49 50        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);51    }52}53 54std::unique_ptr<llm_graph_context> llama_model_chatglm::build_arch_graph(const llm_graph_params & params) const {55    return std::make_unique<graph>(*this, params);56}57 58llama_model_chatglm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {59    const int64_t n_embd_head = hparams.n_embd_head_v();60 61    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());62 63    ggml_tensor * cur;64    ggml_tensor * inpL;65 66    inpL = build_inp_embd(model.tok_embd);67 68    // inp_pos - contains the positions69    ggml_tensor * inp_pos = build_inp_pos();70 71    auto * inp_attn = build_attn_inp_kv();72 73    ggml_tensor * inp_out_ids = build_inp_out_ids();74 75    for (int il = 0; il < n_layer; ++il) {76        ggml_tensor * inpSA = inpL;77 78        cur = build_norm(inpL,79                model.layers[il].attn_norm,80                NULL,81                LLM_NORM_RMS, il);82        cb(cur, "attn_norm", il);83 84        // self-attention85        {86            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,87                    n_embd_head, n_head, n_head_kv, il);88 89            //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);90            Qcur = ggml_rope_ext(91                    ctx0, Qcur, inp_pos, nullptr,92                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,93                    ext_factor, attn_factor, beta_fast, beta_slow94                    );95 96            Kcur = ggml_rope_ext(97                    ctx0, Kcur, inp_pos, nullptr,98                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,99                    ext_factor, attn_factor, beta_fast, beta_slow100                    );101 102            cb(Qcur, "Qcur", il);103            cb(Kcur, "Kcur", il);104            cb(Vcur, "Vcur", il);105 106            cur = build_attn(inp_attn,107                    model.layers[il].wo, NULL, model.layers[il].wo_s,108                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);109        }110 111        if (il == n_layer - 1 && inp_out_ids) {112            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);113            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);114        }115 116        // Add the input117        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);118        cb(ffn_inp, "ffn_inp", il);119 120        // FF121        {122            cur = build_norm(ffn_inp,123                    model.layers[il].ffn_norm,124                    NULL,125                    LLM_NORM_RMS, il);126            cb(cur, "ffn_norm", il);127 128            cur = build_ffn(cur,129                    model.layers[il].ffn_up,   NULL, NULL,130                    NULL,                      NULL, NULL,131                    model.layers[il].ffn_down, NULL, NULL,132                    NULL,133                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);134            cb(cur, "ffn_out", il);135 136        }137 138        cur = ggml_add(ctx0, cur, ffn_inp);139 140        cur = build_cvec(cur, il);141        cb(cur, "l_out", il);142 143        // input for next layer144        inpL = cur;145    }146 147    cur = build_norm(inpL,148            model.output_norm,149            NULL,150            LLM_NORM_RMS, -1);151 152    cb(cur, "result_norm", -1);153    res->t_embd = cur;154 155    cur = build_lora_mm(model.output, cur, model.output_s);156 157    cb(cur, "result_output", -1);158    res->t_logits = cur;159 160    ggml_build_forward_expand(gf, cur);161}162