Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_phi2::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);5 6    switch (hparams.n_layer()) {7        case 24: type = LLM_TYPE_1B; break;8        case 32: type = LLM_TYPE_3B; break;9        default: type = LLM_TYPE_UNKNOWN;10    }11}12 13void llama_model_phi2::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_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);21    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);22    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);23 24    for (int i = 0; i < n_layer; ++i) {25        auto & layer = layers[i];26 27        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);28        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);29 30        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);31 32        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);33        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);34 35        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);36        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);37 38        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);39        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);40    }41}42 43std::unique_ptr<llm_graph_context> llama_model_phi2::build_arch_graph(const llm_graph_params & params) const {44    return std::make_unique<graph>(*this, params);45}46 47llama_model_phi2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {48    const int64_t n_embd_head = hparams.n_embd_head_v();49 50    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());51 52    ggml_tensor * cur;53    ggml_tensor * attn_norm_output;54    ggml_tensor * ffn_output;55    ggml_tensor * inpL;56 57    inpL = build_inp_embd(model.tok_embd);58 59    // inp_pos - contains the positions60    ggml_tensor * inp_pos = build_inp_pos();61 62    auto * inp_attn = build_attn_inp_kv();63 64    ggml_tensor * inp_out_ids = build_inp_out_ids();65 66    for (int il = 0; il < n_layer; ++il) {67        attn_norm_output = build_norm(inpL,68                model.layers[il].attn_norm,69                model.layers[il].attn_norm_b,70                LLM_NORM, il);71        cb(attn_norm_output, "attn_norm", il);72 73        // self-attention74        {75            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], attn_norm_output,76                    n_embd_head, n_head, n_head_kv, il);77            Qcur = ggml_rope_ext(78                    ctx0, Qcur, inp_pos, nullptr,79                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,80                    ext_factor, attn_factor, beta_fast, beta_slow81                    );82 83            Kcur = ggml_rope_ext(84                    ctx0, Kcur, inp_pos, nullptr,85                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,86                    ext_factor, attn_factor, beta_fast, beta_slow87                    );88 89            cb(Qcur, "Qcur", il);90            cb(Kcur, "Kcur", il);91            cb(Vcur, "Vcur", il);92 93            // with phi2, we scale the Q to avoid precision issues94            // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L6695            Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));96 97            cur = build_attn(inp_attn,98                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,99                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);100        }101        if (il == n_layer - 1 && inp_out_ids) {102            cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);103            inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);104            attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);105        }106        // FF107        {108            ffn_output = build_ffn(attn_norm_output,109                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,110                    NULL,                      NULL,                        NULL,111                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,112                    NULL,113                    LLM_FFN_GELU, LLM_FFN_SEQ, il);114            cb(ffn_output, "ffn_out", il);115        }116        cur = ggml_add(ctx0, cur, ffn_output);117        cur = ggml_add(ctx0, cur, inpL);118 119        cur = build_cvec(cur, il);120        cb(cur, "l_out", il);121 122        // input for next layer123        inpL = cur;124    }125    cur = build_norm(inpL,126            model.output_norm,127            model.output_norm_b,128            LLM_NORM, -1);129 130    cb(cur, "result_norm", -1);131    res->t_embd = cur;132 133    cur = build_lora_mm(model.output, cur, model.output_s);134    cb(cur, "result_output_no_bias", -1);135 136    cur = ggml_add(ctx0, cur, model.output_b);137 138    cb(cur, "result_output", -1);139    res->t_logits = cur;140 141    ggml_build_forward_expand(gf, cur);142}143