Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_qwen2::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 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;8        case 28: type = hparams.n_embd == 1536 ? LLM_TYPE_1_5B : LLM_TYPE_7B; break;9        case 32: type = LLM_TYPE_7B; break;10        case 36: type = LLM_TYPE_3B; break;11        case 40: type = hparams.n_head() == 20 ? LLM_TYPE_4B : LLM_TYPE_13B; break;12        case 48: type = LLM_TYPE_14B; break;13        case 64: type = LLM_TYPE_32B; break;14        case 80: type = LLM_TYPE_70B; break;15        default: type = LLM_TYPE_UNKNOWN;16    }17}18 19void llama_model_qwen2::load_arch_tensors(llama_model_loader &) {20    LLAMA_LOAD_LOCALS;21 22    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);23 24    // output25    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);26    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);27    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);28    // if output is NULL, init from the input tok embed29    if (output == NULL) {30        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);31    }32 33    for (int i = 0; i < n_layer; ++i) {34        auto & layer = layers[i];35 36        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);37 38        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);39        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);40 41        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);42 43        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);44        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);45        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);46    }47}48 49std::unique_ptr<llm_graph_context> llama_model_qwen2::build_arch_graph(const llm_graph_params & params) const {50    return std::make_unique<graph>(*this, params);51}52 53llama_model_qwen2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {54    const int64_t n_embd_head = hparams.n_embd_head_v();55 56    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());57    GGML_ASSERT(n_embd_head == n_rot);58 59    ggml_tensor * cur;60    ggml_tensor * inpL;61 62    inpL = build_inp_embd(model.tok_embd);63 64    // inp_pos - contains the positions65    ggml_tensor * inp_pos = build_inp_pos();66 67    auto * inp_attn = build_attn_inp_kv();68 69    ggml_tensor * inp_out_ids = build_inp_out_ids();70 71    for (int il = 0; il < n_layer; ++il) {72        ggml_tensor * inpSA = inpL;73 74        // norm75        cur = build_norm(inpL,76                model.layers[il].attn_norm, NULL,77                LLM_NORM_RMS, il);78        cb(cur, "attn_norm", il);79 80        // self-attention81        {82            // compute Q and K and RoPE them83            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,84                    n_embd_head, n_head, n_head_kv, il);85 86            Qcur = ggml_rope_ext(87                    ctx0, Qcur, inp_pos, nullptr,88                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,89                    ext_factor, attn_factor, beta_fast, beta_slow90                    );91 92            Kcur = ggml_rope_ext(93                    ctx0, Kcur, inp_pos, nullptr,94                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,95                    ext_factor, attn_factor, beta_fast, beta_slow96                    );97 98            cb(Qcur, "Qcur", il);99            cb(Kcur, "Kcur", il);100            cb(Vcur, "Vcur", il);101 102            cur = build_attn(inp_attn,103                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,104                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);105        }106        if (il == n_layer - 1 && inp_out_ids) {107            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);108            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);109        }110        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);111        cb(ffn_inp, "ffn_inp", il);112 113        // feed-forward network114        cur = build_norm(ffn_inp,115                model.layers[il].ffn_norm, NULL,116                LLM_NORM_RMS, il);117        cb(cur, "ffn_norm", il);118 119        cur = build_ffn(cur,120                model.layers[il].ffn_up,   NULL, NULL,121                model.layers[il].ffn_gate, NULL, NULL,122                model.layers[il].ffn_down, NULL, NULL,123                NULL,124                LLM_FFN_SILU, LLM_FFN_PAR, il);125        cb(cur, "ffn_out", il);126 127        cur = ggml_add(ctx0, cur, ffn_inp);128 129        cur = build_cvec(cur, il);130        cb(cur, "l_out", il);131 132        // input for next layer133        inpL = cur;134    }135    cur = inpL;136 137    cur = build_norm(cur,138            model.output_norm, NULL,139            LLM_NORM_RMS, -1);140 141    cb(cur, "result_norm", -1);142    res->t_embd = cur;143 144    // lm_head145    cur = build_lora_mm(model.output, cur, model.output_s);146 147    if (model.output_b != nullptr) {148        cur = ggml_add(ctx0, cur, model.output_b);149    }150    cb(cur, "result_output", -1);151    res->t_logits = cur;152 153    ggml_build_forward_expand(gf, cur);154}155