Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_mpt::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);5    ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);6    ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false);7 8    switch (hparams.n_layer()) {9        case 32: type = LLM_TYPE_7B; break;10        case 48: type = LLM_TYPE_30B; break;11        default: type = LLM_TYPE_UNKNOWN;12    }13}14 15void llama_model_mpt::load_arch_tensors(llama_model_loader &) {16    LLAMA_LOAD_LOCALS;17 18    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);19    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, TENSOR_NOT_REQUIRED);20 21    // output22    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);23    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, TENSOR_NOT_REQUIRED);24 25    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);26    if (!output) {27        output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU28    }29 30    for (int i = 0; i < n_layer; ++i) {31        auto & layer = layers[i];32 33        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);34        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);35 36        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);37        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);38 39        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);40        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);41 42        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);43        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);44 45        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);46        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);47 48        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);49        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);50 51        // FIXME test-llama-archs crashes if q_norm is created52        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);53        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);54 55        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);56        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);57 58        // AWQ ScaleActivation layer59        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, TENSOR_NOT_REQUIRED);60    }61}62 63std::unique_ptr<llm_graph_context> llama_model_mpt::build_arch_graph(const llm_graph_params & params) const {64    return std::make_unique<graph>(*this, params);65}66 67llama_model_mpt::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {68    const int64_t n_embd_head = hparams.n_embd_head_v();69 70    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());71 72    ggml_tensor * cur;73    ggml_tensor * pos;74    ggml_tensor * inpL;75 76    inpL = build_inp_embd(model.tok_embd);77 78    auto * inp_attn = build_attn_inp_kv();79 80    if (model.pos_embd) {81        // inp_pos - contains the positions82        ggml_tensor * inp_pos = build_inp_pos();83        pos                   = ggml_get_rows(ctx0, model.pos_embd, inp_pos);84        cb(pos, "pos_embd", -1);85 86        inpL = ggml_add(ctx0, inpL, pos);87        cb(inpL, "inpL", -1);88    }89 90    ggml_tensor * inp_out_ids = build_inp_out_ids();91 92    for (int il = 0; il < n_layer; ++il) {93        ggml_tensor * attn_norm;94 95        attn_norm = build_norm(inpL, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, il);96        cb(attn_norm, "attn_norm", il);97 98        // self-attention99        {100            cur = attn_norm;101 102            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,103                    n_embd_head, n_head, n_head_kv, il);104 105            // Q/K Layernorm106            if (model.layers[il].attn_q_norm) {107                Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens);108                Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens);109 110                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il);111 112                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, il);113 114                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);115                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);116            }117 118            cb(Qcur, "Qcur", il);119            cb(Kcur, "Kcur", il);120            cb(Vcur, "Vcur", il);121 122            cur = build_attn(inp_attn,123                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,124                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);125        }126 127        if (il == n_layer - 1 && inp_out_ids) {128            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);129            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);130        }131 132        // Add the input133        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);134        cb(ffn_inp, "ffn_inp", il);135 136        // feed forward137        {138            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, il);139            cb(cur, "ffn_norm", il);140            cur = build_ffn(cur,141                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,142                NULL, NULL, NULL,143                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,144                model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, il);145            cb(cur, "ffn_out", il);146        }147 148        cur = ggml_add(ctx0, cur, ffn_inp);149 150        cur = build_cvec(cur, il);151        cb(cur, "l_out", il);152 153        // input for next layer154        inpL = cur;155    }156 157    cur = inpL;158 159    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);160 161    cb(cur, "result_norm", -1);162    res->t_embd = cur;163 164    cur = build_lora_mm(model.output, cur, model.output_s);165 166    cb(cur, "result_output", -1);167    res->t_logits = cur;168 169    ggml_build_forward_expand(gf, cur);170}171