Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_t5::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      hparams.f_norm_rms_eps);5    ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);6 7    uint32_t dec_start_token_id;8    if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {9        hparams.dec_start_token_id = dec_start_token_id;10    }11 12    hparams.dec_n_layer = hparams.n_layer();13    ml.get_key(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer, false);14 15    switch (hparams.n_layer()) {16        case 6:  type = LLM_TYPE_60M;  break; // t5-small17        case 8:  type = LLM_TYPE_80M;  break; // flan-t5-small18        case 12:19            switch (hparams.n_ff()) {20                case 3072: type = LLM_TYPE_220M; break; // t5-base21                case 2048: type = LLM_TYPE_250M; break; // flan-t5-base22                default: type = LLM_TYPE_UNKNOWN;23            } break;24        case 24:25            switch (hparams.n_ff()) {26                case 4096:  type = LLM_TYPE_770M; break; // t5-large27                case 2816:  type = LLM_TYPE_780M; break; // flan-t5-large28                case 16384: type = LLM_TYPE_3B;   break; // t5-3b29                case 5120:  type = LLM_TYPE_3B;   break; // flan-t5-xl30                case 65536: type = LLM_TYPE_11B;  break; // t5-11b31                case 10240: type = LLM_TYPE_11B;  break; // flan-t5-xxl32                default: type = LLM_TYPE_UNKNOWN;33            } break;34        default: type = LLM_TYPE_UNKNOWN;35   }36}37 38void llama_model_t5::load_arch_tensors(llama_model_loader &) {39    LLAMA_LOAD_LOCALS;40 41    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;42 43    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);44 45    // output46    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);47    output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);48 49    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);50    // if output is NULL, init from the input tok embed51    if (output == NULL) {52        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);53    }54 55    // n_layer:     number of encoder_layers56    // dec_n_layer: number of decoder_layers57    const int dec_n_layer = hparams.dec_n_layer;58    if (dec_n_layer > n_layer) {59        layers.resize(dec_n_layer);60    }61 62    // load encoder layers63    for (int i = 0; i < n_layer; ++i) {64        auto & layer = layers[i];65 66        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);67        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);68 69        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);70        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);71        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);72        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);73 74        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);75        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);76        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);77        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);78    }79 80    // load decoder layers81    for (int i = 0; i < dec_n_layer; ++i) {82        auto & layer = layers[i];83 84        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);85        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);86 87        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);88        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);89        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);90        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);91 92        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);93        // this tensor seems to be unused in HF transformers implementation94        layer.attn_rel_b_cross = create_tensor(95            tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);96 97        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);98        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);99        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);100        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);101 102        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);103        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);104        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);105        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);106    }107}108 109std::unique_ptr<llm_graph_context> llama_model_t5::build_arch_graph(const llm_graph_params & params) const {110    switch (params.gtype) {111        case LLM_GRAPH_TYPE_ENCODER:112            return std::make_unique<graph<true>>(*this, params);113        case LLM_GRAPH_TYPE_DEFAULT:114        case LLM_GRAPH_TYPE_DECODER:115            return std::make_unique<graph<false>>(*this, params);116        default:117            GGML_ABORT("invalid graph type");118    };119}120 121template <>122llama_model_t5::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {123    const int64_t n_embd_head = hparams.n_embd_head_v();124    //const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();125 126    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());127 128    ggml_tensor * cur;129    ggml_tensor * inpL;130 131    inpL = build_inp_embd(model.tok_embd);132 133    ggml_tensor * embd_enc       = build_inp_cross_embd();134    ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();135 136    const int64_t n_outputs_enc = embd_enc->ne[1];137 138    auto * inp_attn_self  = build_attn_inp_kv();139    auto * inp_attn_cross = build_attn_inp_cross();140 141    ggml_tensor * inp_out_ids = build_inp_out_ids();142 143    const int64_t dec_n_layer = hparams.dec_n_layer;144 145    for (int il = 0; il < dec_n_layer; ++il) {146        ggml_tensor * inpSA = inpL;147 148        // norm149        cur = build_norm(inpL,150                model.layers[il].attn_norm, NULL,151                LLM_NORM_RMS, il);152        cb(cur, "attn_norm", il);153 154        // self-attention155        {156            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);157 158            ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;159            ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);160 161            cur = build_attn(inp_attn_self,162                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,163                    Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);164            cb(cur, "kqv_out", il);165        }166        cur = ggml_add(ctx0, cur, inpSA);167        cb(cur, "cross_inp", il);168 169        ggml_tensor * inpCA = cur;170 171        // norm172        cur = build_norm(cur,173                model.layers[il].attn_norm_cross, NULL,174                LLM_NORM_RMS, il);175        cb(cur, "attn_norm_cross", il);176 177        // cross-attention178        {179            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_cross, cur);180            cb(Qcur, "Qcur", il);181 182            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);183            cb(Kcur, "Kcur", il);184 185            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_cross, embd_enc);186            cb(Vcur, "Vcur", il);187 188            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);189            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);190            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc);191 192            cur = build_attn(inp_attn_cross,193                    model.layers[il].wo_cross, nullptr, nullptr,194                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);195            cb(cur, "kqv_out", il);196 197            //ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);198            //ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));199 200            //ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);201            //cb(kq, "kq", il);202 203            //kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);204            //cb(kq, "kq_soft_max_ext", il);205 206            //ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));207            //cb(v, "v", il);208 209            //ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);210            //cb(kqv, "kqv", il);211 212            //ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);213            //cb(kqv_merged, "kqv_merged", il);214 215            //cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);216            //cb(cur, "kqv_merged_cont", il);217 218            //ggml_build_forward_expand(gf, cur);219 220            //cur = build_lora_mm(model.layers[il].wo_cross, cur);221            //cb(cur, "kqv_out", il);222        }223        if (il == dec_n_layer - 1 && inp_out_ids) {224            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);225            inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);226        }227        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);228        cb(ffn_inp, "ffn_inp", il);229 230        // feed-forward network231        {232            cur = build_norm(ffn_inp,233                    model.layers[il].ffn_norm, NULL,234                    LLM_NORM_RMS, il);235            cb(cur, "ffn_norm", il);236 237            // T5 uses relu, flan-T5 uses gelu-gated238            cur = build_ffn(cur,239                    model.layers[il].ffn_up,   NULL, NULL,240                    model.layers[il].ffn_gate, NULL, NULL,241                    model.layers[il].ffn_down, NULL, NULL,242                    NULL,243                    model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,244                    model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,245                    il);246            cb(cur, "ffn_out", il);247        }248        cur = ggml_add(ctx0, cur, ffn_inp);249        cb(cur, "ffn_out", il);250 251        cur = build_cvec(cur, il);252        cb(cur, "l_out", il);253 254        // input for next layer255        inpL = cur;256    }257    cur = inpL;258    cb(cur, "result_embd", -1);259 260    cur = build_norm(cur,261            model.output_norm, NULL,262            LLM_NORM_RMS, -1);263 264    cb(cur, "result_norm", -1);265    res->t_embd = cur;266 267    // lm_head268    cur = build_lora_mm(model.output, cur, model.output_s);269 270    cb(cur, "result_output", -1);271    res->t_logits = cur;272 273    ggml_build_forward_expand(gf, cur);274}275 276template <>277llama_model_t5::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {278    const int64_t n_embd_head = hparams.n_embd_head_v();279 280    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());281 282    ggml_tensor * cur;283    ggml_tensor * inpL;284 285    inpL = build_inp_embd(model.tok_embd);286 287    ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();288 289    auto * inp_attn = build_attn_inp_no_cache();290 291    ggml_tensor * inp_out_ids = build_inp_out_ids();292 293    for (int il = 0; il < n_layer; ++il) {294        ggml_tensor * inpSA = inpL;295 296        // norm297        cur = build_norm(inpL,298                model.layers[il].attn_norm_enc, NULL,299                LLM_NORM_RMS, il);300        cb(cur, "attn_norm", il);301 302        // self-attention303        {304            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);305            cb(Qcur, "Qcur", il);306 307            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);308            cb(Kcur, "Kcur", il);309 310            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);311            cb(Vcur, "Vcur", il);312 313            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);314            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);315            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);316 317            ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;318            ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);319 320            cur = build_attn(inp_attn,321                    model.layers[il].wo_enc, nullptr, nullptr,322                    Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);323            cb(cur, "kqv_out", il);324        }325        if (il == n_layer - 1 && inp_out_ids) {326            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);327            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);328        }329        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);330        cb(ffn_inp, "ffn_inp", il);331 332        // feed-forward network333        {334            cur = build_norm(ffn_inp,335                    model.layers[il].ffn_norm_enc, NULL,336                    LLM_NORM_RMS, il);337            cb(cur, "ffn_norm", il);338 339            // T5 uses relu, flan-T5 uses gelu-gated340            cur = build_ffn(cur,341                    model.layers[il].ffn_up_enc,   NULL, NULL,342                    model.layers[il].ffn_gate_enc, NULL, NULL,343                    model.layers[il].ffn_down_enc, NULL, NULL,344                    NULL,345                    model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,346                    model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,347                    il);348            cb(cur, "ffn_out", il);349        }350        cur = ggml_add(ctx0, cur, ffn_inp);351        cb(cur, "ffn_out", il);352 353        cur = build_cvec(cur, il);354        cb(cur, "l_out", il);355 356        // input for next layer357        inpL = cur;358    }359    cur = inpL;360    cb(cur, "result_embd", -1);361 362    cur = build_norm(cur,363            model.output_norm_enc, NULL,364            LLM_NORM_RMS, -1);365 366    cb(cur, "result_norm", -1);367    res->t_embd = cur;368 369    ggml_build_forward_expand(gf, cur);370}371