Introduce C-style API (#370)

* Major refactoring - introduce C-style API

* Clean up

* Add <cassert>

* Add <iterator>

* Add <algorithm> ....

* Fix timing reporting and accumulation

* Measure eval time only for single-token calls

* Change llama_tokenize return meaning

1 files changed, 1565 insertions, 0 deletions

diff --git a/llama.cpp b/llama.cpp
new file mode 100644
index 00000000..08dfcb31
--- /dev/null
+++ b/llama.cpp
@@ -0,0 +1,1565 @@
+#include "llama.h"
+
+#include "ggml.h"
+
+#include <cinttypes>
+#include <fstream>
+#include <random>
+#include <unordered_map>
+#include <queue>
+#include <regex>
+#include <cassert>
+
+// determine number of model parts based on the dimension
+static const std::unordered_map<int, int> LLAMA_N_PARTS = {
+    { 4096, 1 },
+    { 5120, 2 },
+    { 6656, 4 },
+    { 8192, 8 },
+};
+
+// default hparams (LLaMA 7B)
+struct llama_hparams {
+    int32_t n_vocab = 32000;
+    int32_t n_ctx   = 512;   // this is provided as user input?
+    int32_t n_embd  = 4096;
+    int32_t n_mult  = 256;
+    int32_t n_head  = 32;
+    int32_t n_layer = 32;
+    int32_t n_rot   = 64;
+    int32_t f16     = 1;
+};
+
+struct llama_layer {
+    // normalization
+    struct ggml_tensor * attention_norm;
+
+    // attention
+    struct ggml_tensor * wq;
+    struct ggml_tensor * wk;
+    struct ggml_tensor * wv;
+    struct ggml_tensor * wo;
+
+    // normalization
+    struct ggml_tensor * ffn_norm;
+
+    // ff
+    struct ggml_tensor * w1;
+    struct ggml_tensor * w2;
+    struct ggml_tensor * w3;
+};
+
+struct llama_model {
+    llama_hparams hparams;
+
+    struct ggml_tensor * tok_embeddings;
+
+    struct ggml_tensor * norm;
+    struct ggml_tensor * output;
+
+    std::vector<llama_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::unordered_map<std::string, struct ggml_tensor *> tensors;
+};
+
+struct llama_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    struct token_score {
+        token tok;
+        float score;
+    };
+
+    std::unordered_map<token, id> token_to_id;
+    std::vector<token_score> id_to_token;
+};
+
+struct llama_context {
+    std::mt19937 rng;
+
+    int64_t t_load_us = 0;
+    int64_t t_start_us = 0;
+
+    int64_t t_sample_us = 0;
+    int64_t t_eval_us   = 0;
+
+    int32_t n_sample = 0; // number of tokens sampled
+    int32_t n_eval   = 0; // number of eval calls
+
+    llama_model model;
+    llama_vocab vocab;
+
+    size_t mem_per_token = 0;
+
+    // decode output (2-dimensional array: [n_tokens][n_vocab])
+    std::vector<float> logits;
+    bool logits_all = false;
+};
+
+struct llama_context_params llama_context_default_params() {
+    struct llama_context_params result = {
+        /*.n_ctx      =*/ 512,
+        /*.n_parts    =*/ -1,
+        /*.seed       =*/ 0,
+        /*.f16_kv     =*/ false,
+        /*.logits_all =*/ false,
+        /*.vocab_only =*/ false,
+    };
+
+    return result;
+}
+
+//
+// model loading
+//
+
+static bool llama_model_load(
+        const std::string & fname,
+        llama_context & lctx,
+        int n_ctx,
+        int n_parts,
+        ggml_type memory_type,
+        bool vocab_only) {
+    fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
+
+    const int64_t t_start_us = ggml_time_us();
+
+    lctx.t_start_us = t_start_us;
+
+    std::vector<char> f_buf(1024*1024);
+
+    auto & model = lctx.model;
+    auto & vocab = lctx.vocab;
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
+    if (!fin) {
+        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname.c_str());
+            return false;
+        }
+        if (magic != LLAMA_FILE_MAGIC) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+
+        uint32_t format_version;
+        fin.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != LLAMA_FILE_VERSION) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
+                    __func__, fname.c_str(), format_version, LLAMA_FILE_VERSION);
+            return false;
+        }
+    }
+
+    int n_ff = 0;
+
+    // load hparams
+    {
+        auto & hparams = model.hparams;
+
+        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        //fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fin.read((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
+        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fin.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
+        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        hparams.n_ctx = n_ctx;
+
+        n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
+
+        if (n_parts < 1) {
+            n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
+        }
+
+        // temp warning to tell the user to use "--n_parts"
+        if (hparams.f16 == 4 && n_parts != 1) {
+            fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
+            fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
+        }
+
+        fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        fprintf(stderr, "%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        fprintf(stderr, "%s: n_mult  = %d\n", __func__, hparams.n_mult);
+        fprintf(stderr, "%s: n_head  = %d\n", __func__, hparams.n_head);
+        fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
+        fprintf(stderr, "%s: n_rot   = %d\n", __func__, hparams.n_rot);
+        fprintf(stderr, "%s: f16     = %d\n", __func__, hparams.f16);
+        fprintf(stderr, "%s: n_ff    = %d\n", __func__, n_ff);
+        fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
+    }
+
+    // load vocab
+    {
+        std::string word;
+        vocab.id_to_token.resize(model.hparams.n_vocab);
+        std::vector<char> tmp(64);
+
+        for (int i = 0; i < model.hparams.n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            word.resize(len);
+            if (len > 0) {
+                tmp.resize(len);
+                fin.read(tmp.data(), len);
+                word.assign(tmp.data(), len);
+            } else {
+                word.clear();
+            }
+
+            float score;
+            fin.read((char *) &score, sizeof(score));
+
+            vocab.token_to_id[word] = i;
+
+            auto &tok_score = vocab.id_to_token[i];
+            tok_score.tok = word;
+            tok_score.score = score;
+        }
+    }
+
+    if (vocab_only) {
+        return true;
+    }
+
+    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
+    // in order to save memory and also to speed up the computation
+    // wtype is for per-layer weights, while vtype is for other weights
+    ggml_type wtype, vtype;
+    switch (model.hparams.f16) {
+        case 0: wtype = vtype = GGML_TYPE_F32;  break;
+        case 1: wtype = vtype = GGML_TYPE_F16;  break;
+        case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
+        case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
+        case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
+        default:
+                {
+                    fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
+                            __func__, fname.c_str(), model.hparams.f16);
+                    return false;
+                }
+    }
+
+    auto & ctx = model.ctx;
+
+    size_t ctx_size = 0;
+
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
+
+        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
+
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo
+
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm
+
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
+
+        ctx_size += (5 + 10*n_layer)*256; // object overhead
+
+        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+    }
+
+    // create the ggml context
+    {
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ ctx_size,
+            /*.mem_buffer =*/ NULL,
+        };
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
+    // prepare memory for the weights
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_vocab = hparams.n_vocab;
+
+        model.layers.resize(n_layer);
+
+        model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
+
+        model.norm   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model.output = ggml_new_tensor_2d(ctx, vtype,         n_embd, n_vocab);
+
+        // map by name
+        model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
+
+        model.tensors["norm.weight"]   = model.norm;
+        model.tensors["output.weight"] = model.output;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model.layers[i];
+
+            layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+            layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+
+            layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+            layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
+            layer.w2 = ggml_new_tensor_2d(ctx, wtype,   n_ff, n_embd);
+            layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
+
+            // map by name
+            model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;
+
+            model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
+            model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
+            model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
+            model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
+
+            model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;
+
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
+        }
+    }
+
+    // key + value memory
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+
+        const int n_mem      = n_layer*n_ctx;
+        const int n_elements = n_embd*n_mem;
+
+        model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
+        model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+        fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    const size_t file_offset = fin.tellg();
+
+    fin.close();
+
+    std::vector<uint8_t> tmp;
+
+    for (int i = 0; i < n_parts; ++i) {
+        const int part_id = i;
+        //const int part_id = n_parts - i - 1;
+
+        std::string fname_part = fname;
+        if (i > 0) {
+            fname_part += "." + std::to_string(i);
+        }
+
+        fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
+
+        fin = std::ifstream(fname_part, std::ios::binary);
+        fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
+        fin.seekg(file_offset);
+
+        // load weights
+        {
+            int n_tensors = 0;
+            size_t total_size = 0;
+
+            fprintf(stderr, "%s: ", __func__);
+
+            while (true) {
+                int32_t n_dims;
+                int32_t length;
+                int32_t ftype;
+
+                fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+                fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+                fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+                if (fin.eof()) {
+                    break;
+                }
+
+                int32_t nelements = 1;
+                int32_t ne[2] = { 1, 1 };
+                for (int i = 0; i < n_dims; ++i) {
+                    fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                    nelements *= ne[i];
+                }
+
+                std::string name(length, 0);
+                fin.read(&name[0], length);
+
+                if (model.tensors.find(name.data()) == model.tensors.end()) {
+                    fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+                    return false;
+                }
+
+                // split_type = 0: split by columns
+                // split_type = 1: split by rows
+                int split_type = 0;
+
+                // split_type = 0:
+                // regex:
+                //   - tok_embeddings.*
+                //   - layers.*.attention.wo.weight
+                //   - layers.*.feed_forward.w2.weight
+
+                // split_type = 1:
+                // regex:
+                //   - output.*
+                //   - layers.*.attention.wq.weight
+                //   - layers.*.attention.wk.weight
+                //   - layers.*.attention.wv.weight
+                //   - layers.*.feed_forward.w1.weight
+                //   - layers.*.feed_forward.w3.weight
+                if (name.find("tok_embeddings") != std::string::npos) {
+                    split_type = 0;
+                } else if (name.find("layers") != std::string::npos) {
+                    if (name.find("attention.wo.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else {
+                        split_type = 1;
+                    }
+                } else if (name.find("output") != std::string::npos) {
+                    split_type = 1;
+                }
+
+                auto tensor = model.tensors[name.data()];
+
+                if (n_dims == 1) {
+                    if (ggml_nelements(tensor) != nelements) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                        return false;
+                    }
+                } else {
+                    if (ggml_nelements(tensor)/n_parts != nelements) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                        return false;
+                    }
+                }
+
+                if (n_dims == 1) {
+                    if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+                        return false;
+                    }
+                } else {
+                    if (split_type == 0) {
+                        if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
+                            return false;
+                        }
+                    } else {
+                        if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
+                            return false;
+                        }
+                    }
+                }
+
+                if (0) {
+                    static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
+                }
+
+                size_t bpe = 0;
+
+                switch (ftype) {
+                    case 0: bpe = ggml_type_size(GGML_TYPE_F32);  break;
+                    case 1: bpe = ggml_type_size(GGML_TYPE_F16);  break;
+                    case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
+                    case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
+                    default:
+                            {
+                                fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
+                                return false;
+                            }
+                };
+
+                if (n_dims == 1 || n_parts == 1) {
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                                __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                        return false;
+                    }
+
+                    if (part_id == 0) {
+                        fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+                    } else {
+                        fin.seekg(ggml_nbytes(tensor), std::ios::cur);
+                    }
+
+                    total_size += ggml_nbytes(tensor);
+                } else {
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                                __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
+                        return false;
+                    }
+
+                    if (split_type == 0) {
+                        const int np0 = ne[0];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                        assert(row_size == tensor->nb[1]);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = i1*row_size;
+                            const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
+                        }
+                    } else {
+                        const int np1 = ne[1];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = (i1 + part_id*np1)*row_size;
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
+                        }
+                    }
+
+                    total_size += ggml_nbytes(tensor)/n_parts;
+                }
+
+                //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+                if (++n_tensors % 8 == 0) {
+                    fprintf(stderr, ".");
+                    fflush(stderr);
+                }
+            }
+
+            fprintf(stderr, " done\n");
+
+            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
+        }
+
+        fin.close();
+    }
+
+    lctx.logits.reserve(lctx.model.hparams.n_ctx);
+
+    lctx.t_load_us = ggml_time_us() - t_start_us;
+
+    return true;
+}
+
+// evaluate the transformer
+//
+//   - lctx:      llama context
+//   - tokens:    new batch of tokens to process
+//   - n_past:    the context size so far
+//   - n_threads: number of threads to use
+//
+static bool llama_eval_internal(
+        llama_context & lctx,
+    const llama_token * tokens,
+            const int   n_tokens,
+            const int   n_past,
+            const int   n_threads) {
+    const int64_t t_start_us = ggml_time_us();
+
+    const int N = n_tokens;
+
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+
+    const int n_embd  = hparams.n_embd;
+    const int n_layer = hparams.n_layer;
+    const int n_ctx   = hparams.n_ctx;
+    const int n_head  = hparams.n_head;
+    const int n_vocab = hparams.n_vocab;
+    const int n_rot   = hparams.n_embd/hparams.n_head;
+
+    auto & mem_per_token = lctx.mem_per_token;
+
+    // TODO: fix this hardcoded size
+    static size_t buf_size = 512u*1024*1024;
+    static void * buf = malloc(buf_size);
+
+    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
+        const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
+        //fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
+
+        // reallocate
+        buf_size = buf_size_new;
+        buf = realloc(buf, buf_size);
+        if (buf == nullptr) {
+            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+            return false;
+        }
+    }
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+    ggml_cgraph gf = {};
+    gf.n_threads = n_threads;
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, tokens, N*ggml_element_size(embd));
+
+    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * inpSA = inpL;
+
+        struct ggml_tensor * cur;
+
+        // norm
+        {
+            cur = ggml_rms_norm(ctx0, inpL);
+
+            // cur = attention_norm*cur
+            cur = ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
+                        cur);
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+
+            // store key and value to memory
+            if (N >= 1) {
+                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
+
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_rope(ctx0,
+                            ggml_cpy(ctx0,
+                                Qcur,
+                                ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
+                            n_past, n_rot, 0),
+                        0, 2, 1, 3);
+
+            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
+            struct ggml_tensor * K =
+                ggml_permute(ctx0,
+                        ggml_rope(ctx0,
+                            ggml_reshape_3d(ctx0,
+                                ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                                n_embd/n_head, n_head, n_past + N),
+                            n_past, n_rot, 1),
+                        0, 2, 1, 3);
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+            // KQ_scaled = KQ / sqrt(n_embd/n_head)
+            struct ggml_tensor * KQ_scaled =
+                ggml_scale(ctx0,
+                        KQ,
+                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
+                        );
+
+            // KQ_masked = mask_past(KQ_scaled)
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+
+            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
+            struct ggml_tensor * V_trans =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
+
+            // KQV = transpose(V) * KQ_soft_max
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_embd, N)
+            cur = ggml_cpy(ctx0,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+
+            // projection (no bias)
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].wo,
+                    cur);
+        }
+
+        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_rms_norm(ctx0, inpFF);
+
+                // cur = ffn_norm*cur
+                cur = ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
+                        cur);
+            }
+
+            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
+                    model.layers[il].w3,
+                    cur);
+
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w1,
+                    cur);
+
+            // SILU activation
+            cur = ggml_silu(ctx0, cur);
+
+            cur = ggml_mul(ctx0, cur, tmp);
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w2,
+                    cur);
+        }
+
+        cur  = ggml_add(ctx0, cur, inpFF);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    // norm
+    {
+        inpL = ggml_rms_norm(ctx0, inpL);
+
+        // inpL = norm*inpL
+        inpL = ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.norm, inpL),
+                    inpL);
+    }
+
+    // lm_head
+    {
+        inpL = ggml_mul_mat(ctx0, model.output, inpL);
+    }
+
+    // logits -> probs
+    //inpL = ggml_soft_max(ctx0, inpL);
+
+    // run the computation
+    ggml_build_forward_expand(&gf, inpL);
+    ggml_graph_compute       (ctx0, &gf);
+
+    //if (n_past%100 == 0) {
+    //    ggml_graph_print   (&gf);
+    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //}
+
+    //embd_w.resize(n_vocab*N);
+    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+    auto & logits_out = lctx.logits;
+
+    if (lctx.logits_all) {
+        logits_out.resize(n_vocab * N);
+        memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+    } else {
+        // return result for just the last token
+        logits_out.resize(n_vocab);
+        memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+    }
+
+    if (mem_per_token == 0) {
+        mem_per_token = ggml_used_mem(ctx0)/N;
+    }
+    //fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    // measure the performance only for the single-token evals
+    if (N == 1) {
+        lctx.t_eval_us += ggml_time_us() - t_start_us;
+        lctx.n_eval++;
+    }
+
+    return true;
+}
+
+//
+// tokenizer
+//
+
+static size_t utf8_len(char src) {
+    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
+    return lookup[highbits];
+}
+
+struct llama_sp_symbol {
+    using index = int;
+    index prev;
+    index next;
+    const char * text;
+    size_t n;
+};
+
+struct llama_sp_bigram {
+    struct comparator {
+        bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
+            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
+        }
+    };
+    using queue_storage = std::vector<llama_sp_bigram>;
+    using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
+    llama_sp_symbol::index left;
+    llama_sp_symbol::index right;
+    float score;
+    size_t size;
+};
+
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+struct llama_tokenizer {
+    llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // split string into utf8 chars
+        int index = 0;
+        size_t offs = 0;
+        while (offs < text.size()) {
+            llama_sp_symbol sym;
+            size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
+            sym.text = text.c_str() + offs;
+            sym.n = char_len;
+            offs += char_len;
+            sym.prev = index - 1;
+            sym.next = offs == text.size() ? -1 : index + 1;
+            index++;
+            symbols_.emplace_back(std::move(sym));
+        }
+
+        // seed the work queue with all possible 2-character tokens.
+        for (size_t i = 1; i < symbols_.size(); ++i) {
+            try_add_bigram(i - 1, i);
+        }
+
+        // keep substituting the highest frequency pairs for as long as we can.
+        while (!work_queue_.empty()) {
+            auto bigram = work_queue_.top();
+            work_queue_.pop();
+
+            auto & left_sym = symbols_[bigram.left];
+            auto & right_sym = symbols_[bigram.right];
+
+            // if one of the symbols already got merged, skip it.
+            if (left_sym.n == 0 || right_sym.n == 0 ||
+                left_sym.n + right_sym.n != bigram.size) {
+                continue;
+            }
+
+            // merge the right sym into the left one
+            left_sym.n += right_sym.n;
+            right_sym.n = 0;
+
+            //printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+
+            // remove the right sym from the chain
+            left_sym.next = right_sym.next;
+            if (right_sym.next >= 0) {
+                symbols_[right_sym.next].prev = bigram.left;
+            }
+
+            // find more substitutions
+            try_add_bigram(left_sym.prev, bigram.left);
+            try_add_bigram(bigram.left, left_sym.next);
+        }
+
+        for (int i = 0; i != -1; i = symbols_[i].next) {
+            auto & symbol = symbols_[i];
+            auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
+
+            if (token == vocab_.token_to_id.end()) {
+                // output any symbols that did not form tokens as bytes.
+                for (int j = 0; j < (int) symbol.n; ++j) {
+                    llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+                    output.push_back(token_id);
+                }
+            } else {
+                output.push_back((*token).second);
+            }
+        }
+    }
+
+private:
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
+        auto token = vocab_.token_to_id.find(text);
+
+        if (token == vocab_.token_to_id.end()) {
+            return;
+        }
+
+        if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
+            return;
+        }
+
+        const auto &tok_score = vocab_.id_to_token[(*token).second];
+
+        llama_sp_bigram bigram;
+        bigram.left = left;
+        bigram.right = right;
+        bigram.score = tok_score.score;
+        bigram.size = text.size();
+        work_queue_.push(bigram);
+    }
+
+    const llama_vocab & vocab_;
+    std::vector<llama_sp_symbol> symbols_;
+    llama_sp_bigram::queue work_queue_;
+};
+
+static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
+    llama_tokenizer tokenizer(vocab);
+    std::vector<llama_vocab::id> output;
+
+    if (text.size() == 0) {
+        return output;
+    }
+
+    if (bos) {
+        output.push_back(1);
+    }
+
+    tokenizer.tokenize(text, output);
+    return output;
+}
+
+//
+// sampling
+//
+
+static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
+    // find the top k tokens
+    std::partial_sort(
+            logits_id.begin(),
+            logits_id.begin() + top_k, logits_id.end(),
+            [](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    logits_id.resize(top_k);
+}
+
+static llama_vocab::id llama_sample_top_p_top_k(
+        llama_context & lctx,
+        const std::vector<llama_vocab::id> & last_n_tokens,
+        int top_k,
+        double top_p,
+        double temp,
+        double repeat_penalty) {
+    auto & rng = lctx.rng;
+
+    const auto & vocab = lctx.vocab;
+    const auto & logits = lctx.logits;
+
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, llama_vocab::id>> logits_id;
+    logits_id.reserve(n_logits);
+
+    {
+        const double scale = 1.0/temp;
+        for (int i = 0; i < n_logits; ++i) {
+            // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
+            // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
+            if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
+                // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
+                if (logits[i] < 0.0) {
+                    logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
+                } else {
+                    logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
+                }
+            } else {
+                logits_id.push_back(std::make_pair(logits[i]*scale, i));
+            }
+        }
+    }
+
+    sample_top_k(logits_id, top_k);
+
+    double maxl = -std::numeric_limits<double>::infinity();
+    for (const auto & kv : logits_id) {
+        maxl = std::max(maxl, kv.first);
+    }
+
+    // compute probs for the top k tokens
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    double sum = 0.0;
+    for (const auto & kv : logits_id) {
+        double p = exp(kv.first - maxl);
+        probs.push_back(p);
+        sum += p;
+    }
+
+    // normalize the probs
+    for (auto & p : probs) {
+        p /= sum;
+    }
+
+    if (top_p < 1.0f) {
+        double cumsum = 0.0f;
+        for (int i = 0; i < (int) probs.size(); i++) {
+            cumsum += probs[i];
+            if (cumsum >= top_p) {
+                probs.resize(i + 1);
+                logits_id.resize(i + 1);
+                break;
+            }
+        }
+
+        cumsum = 1.0/cumsum;
+        for (int i = 0; i < (int) probs.size(); i++) {
+            probs[i] *= cumsum;
+        }
+    }
+
+    //printf("\n");
+    //for (int i = 0; i < (int) 10; i++) {
+    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
+    //}
+    //printf("\n\n");
+    //exit(0);
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}
+
+//
+// quantization
+//
+
+// TODO: reuse code from the llama_model_load() somehow
+bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype, int qk) {
+    ggml_type type = GGML_TYPE_Q4_1;
+
+    switch (itype) {
+        case 2: type = GGML_TYPE_Q4_0; break;
+        case 3: type = GGML_TYPE_Q4_1; break;
+        default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1;
+    };
+
+    if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) {
+        fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type);
+        return false;
+    }
+
+    llama_vocab vocab;
+
+    printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
+
+    auto finp = std::ifstream(fname_inp, std::ios::binary);
+    if (!finp) {
+        fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
+        return false;
+    }
+
+    auto fout = std::ofstream(fname_out, std::ios::binary);
+    if (!fout) {
+        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        finp.read((char *) &magic, sizeof(magic));
+        if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname_inp.c_str());
+            return false;
+        }
+        if (magic != LLAMA_FILE_MAGIC) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
+            return false;
+        }
+
+        fout.write((char *) &magic, sizeof(magic));
+
+        uint32_t format_version;
+        finp.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != LLAMA_FILE_VERSION) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
+                    __func__, fname_inp.c_str(), format_version, LLAMA_FILE_VERSION);
+            return false;
+        }
+
+        fout.write((char *) &format_version, sizeof(format_version));
+    }
+
+    llama_hparams hparams;
+
+    // load hparams
+    {
+        finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        //finp.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        finp.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        finp.read((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
+        finp.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        finp.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
+        finp.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        printf("%s: n_mult  = %d\n", __func__, hparams.n_mult);
+        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
+        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
+
+        fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        //fout.write((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fout.write((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fout.write((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
+        fout.write((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fout.write((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
+        fout.write((char *) &itype,           sizeof(hparams.f16));
+    }
+
+    // load vocab
+    {
+        const int32_t n_vocab = hparams.n_vocab;
+
+        if (n_vocab != hparams.n_vocab) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+                    __func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
+            return false;
+        }
+
+        std::string word;
+        vocab.id_to_token.resize(n_vocab);
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            finp.read ((char *) &len, sizeof(len));
+            fout.write((char *) &len, sizeof(len));
+
+            word.resize(len);
+            finp.read ((char *) word.data(), len);
+            fout.write((char *) word.data(), len);
+
+            float score;
+            finp.read ((char *) &score, sizeof(score));
+            fout.write((char *) &score, sizeof(score));
+
+            vocab.token_to_id[word] = i;
+
+            auto &tok_score = vocab.id_to_token[i];
+            tok_score.tok = word;
+            tok_score.score = score;
+        }
+    }
+
+    // load weights
+    {
+        size_t total_size_org = 0;
+        size_t total_size_new = 0;
+
+        std::vector<float> work;
+
+        std::vector<uint8_t>     data_u8;
+        std::vector<ggml_fp16_t> data_f16;
+        std::vector<float>       data_f32;
+
+        std::vector<int64_t> hist_all(1 << 4, 0);
+
+        while (true) {
+            int32_t n_dims;
+            int32_t length;
+            int32_t ftype;
+
+            finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            finp.read(reinterpret_cast<char *>(&length), sizeof(length));
+            finp.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+            if (finp.eof()) {
+                break;
+            }
+
+            int32_t nelements = 1;
+            int32_t ne[2] = { 1, 1 };
+            for (int i = 0; i < n_dims; ++i) {
+                finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                nelements *= ne[i];
+            }
+
+            std::string name(length, 0);
+            finp.read (&name[0], length);
+
+            {
+                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+                printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
+            }
+
+            // regexes of tensor names to be quantized
+            const std::vector<std::string> k_names = {
+                ".*weight",
+            };
+
+            bool quantize = false;
+            for (const auto & s : k_names) {
+                if (std::regex_match(name, std::regex(s))) {
+                    quantize = true;
+                    break;
+                }
+            }
+
+            // quantize only 2D tensors
+            quantize &= (n_dims == 2);
+
+            if (quantize) {
+                if (ftype != 0 && ftype != 1) {
+                    fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype);
+                    return false;
+                }
+
+                if (ftype == 1) {
+                    data_f16.resize(nelements);
+                    finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
+                    data_f32.resize(nelements);
+                    for (int i = 0; i < nelements; ++i) {
+                        data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
+                    }
+                } else {
+                    data_f32.resize(nelements);
+                    finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
+                }
+
+                ftype = itype;
+            } else {
+                const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t);
+
+                data_u8.resize(nelements*bpe);
+                finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
+            }
+
+            fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            fout.write(reinterpret_cast<char *>(&length), sizeof(length));
+            fout.write(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+            for (int i = 0; i < n_dims; ++i) {
+                fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+            }
+            fout.write(&name[0], length);
+
+            if (quantize) {
+                printf("quantizing .. ");
+                work.resize(nelements); // for quantization
+
+                size_t cur_size = 0;
+                std::vector<int64_t> hist_cur(1 << 4, 0);
+
+                switch (type) {
+                    case GGML_TYPE_Q4_0:
+                        {
+                            cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
+                        } break;
+                    case GGML_TYPE_Q4_1:
+                        {
+                            cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
+                        } break;
+                    default:
+                        {
+                            fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type);
+                            return false;
+                        }
+                }
+
+                fout.write(reinterpret_cast<char *>(work.data()), cur_size);
+                total_size_new += cur_size;
+
+                printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
+                for (int i = 0; i < (int) hist_cur.size(); ++i) {
+                    hist_all[i] += hist_cur[i];
+                }
+
+                for (int i = 0; i < (int) hist_cur.size(); ++i) {
+                    printf("%5.3f ", hist_cur[i] / (float)nelements);
+                }
+                printf("\n");
+            } else {
+                printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
+                fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
+                total_size_new += data_u8.size();
+            }
+
+            total_size_org += nelements * sizeof(float);
+        }
+
+        printf("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
+        printf("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
+
+        {
+            int64_t sum_all = 0;
+            for (int i = 0; i < (int) hist_all.size(); ++i) {
+                sum_all += hist_all[i];
+            }
+
+            printf("%s: hist: ", __func__);
+            for (int i = 0; i < (int) hist_all.size(); ++i) {
+                printf("%5.3f ", hist_all[i] / (float)sum_all);
+            }
+            printf("\n");
+        }
+    }
+
+    finp.close();
+    fout.close();
+
+    return true;
+}
+
+//
+// interface implementation
+//
+
+struct llama_context * llama_init_from_file(
+                             const char * path_model,
+            struct llama_context_params   params) {
+    ggml_time_init();
+
+    llama_context * ctx = new llama_context;
+
+    ctx->rng = std::mt19937(params.seed);
+    ctx->logits_all = params.logits_all;
+
+    ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory, params.vocab_only)) {
+        fprintf(stderr, "%s: failed to load model\n", __func__);
+        delete ctx;
+        return nullptr;
+    }
+
+    return ctx;
+}
+
+void llama_free(struct llama_context * ctx) {
+    ggml_free(ctx->model.ctx);
+
+    delete ctx;
+}
+
+int llama_model_quantize(
+        const char * fname_inp,
+        const char * fname_out,
+               int   itype,
+               int   qk) {
+    if (!llama_model_quantize_internal(fname_inp, fname_out, itype, qk)) {
+        fprintf(stderr, "%s: failed to quantize\n", __func__);
+        return 1;
+    }
+
+    return 0;
+}
+
+int llama_eval(
+        struct llama_context * ctx,
+           const llama_token * tokens,
+                         int   n_tokens,
+                         int   n_past,
+                         int   n_threads) {
+    if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
+        fprintf(stderr, "%s: failed to eval\n", __func__);
+        return 1;
+    }
+
+    return 0;
+}
+
+int llama_tokenize(
+        struct llama_context * ctx,
+                  const char * text,
+                 llama_token * tokens,
+                         int   n_max_tokens,
+                        bool   add_bos) {
+    auto res = llama_tokenize(ctx->vocab, text, add_bos);
+
+    if (n_max_tokens < (int) res.size()) {
+        fprintf(stderr, "%s: too many tokens\n", __func__);
+        return -((int) res.size());
+    }
+
+    for (size_t i = 0; i < res.size(); i++) {
+        tokens[i] = res[i];
+    }
+
+    return res.size();
+}
+
+int llama_n_vocab(struct llama_context * ctx) {
+    return ctx->vocab.id_to_token.size();
+}
+
+int llama_n_ctx(struct llama_context * ctx) {
+    return ctx->model.hparams.n_ctx;
+}
+
+float * llama_get_logits(struct llama_context * ctx) {
+    return ctx->logits.data();
+}
+
+const char * llama_token_to_str(struct llama_context * ctx, llama_token token) {
+    if (token >= llama_n_vocab(ctx)) {
+        return nullptr;
+    }
+
+    return ctx->vocab.id_to_token[token].tok.c_str();
+}
+
+llama_token llama_token_bos() {
+    return 1;
+}
+
+llama_token llama_token_eos() {
+    return 2;
+}
+
+llama_token llama_sample_top_p_top_k(
+          llama_context * ctx,
+      const llama_token * last_n_tokens_data,
+                    int   last_n_tokens_size,
+                    int   top_k,
+                 double   top_p,
+                 double   temp,
+                 double   repeat_penalty) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    llama_token result = 0;
+
+    // TODO: avoid this ...
+    const auto last_n_tokens = std::vector<llama_token>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
+
+    result = llama_sample_top_p_top_k(
+            *ctx,
+            last_n_tokens,
+            top_k,
+            top_p,
+            temp,
+            repeat_penalty);
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    ctx->n_sample++;
+
+    return result;
+}
+
+
+void llama_print_timings(struct llama_context * ctx) {
+    const int64_t t_end_us = ggml_time_us();
+
+    const int32_t n_sample = std::max(1, ctx->n_sample);
+    const int32_t n_eval   = std::max(1, ctx->n_eval);
+
+    fprintf(stderr, "\n");
+    fprintf(stderr, "%s:     load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
+    fprintf(stderr, "%s:   sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample);
+    fprintf(stderr, "%s:     eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us,   n_eval,   1e-3f * ctx->t_eval_us   / n_eval);
+    fprintf(stderr, "%s:    total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
+}
+
+void llama_reset_timings(struct llama_context * ctx) {
+    ctx->t_start_us = ggml_time_us();
+
+    ctx->t_sample_us = ctx->n_sample = 0;
+    ctx->t_eval_us   = ctx->n_eval   = 0;
+}
+
+const char * llama_print_system_info(void) {
+    static std::string s;
+
+    s  = "";
+    s += "AVX = "       + std::to_string(ggml_cpu_has_avx())       + " | ";
+    s += "AVX2 = "      + std::to_string(ggml_cpu_has_avx2())      + " | ";
+    s += "AVX512 = "    + std::to_string(ggml_cpu_has_avx512())    + " | ";
+    s += "FMA = "       + std::to_string(ggml_cpu_has_fma())       + " | ";
+    s += "NEON = "      + std::to_string(ggml_cpu_has_neon())      + " | ";
+    s += "ARM_FMA = "   + std::to_string(ggml_cpu_has_arm_fma())   + " | ";
+    s += "F16C = "      + std::to_string(ggml_cpu_has_f16c())      + " | ";
+    s += "FP16_VA = "   + std::to_string(ggml_cpu_has_fp16_va())   + " | ";
+    s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
+    s += "BLAS = "      + std::to_string(ggml_cpu_has_blas())      + " | ";
+    s += "SSE3 = "      + std::to_string(ggml_cpu_has_sse3())      + " | ";
+    s += "VSX = "       + std::to_string(ggml_cpu_has_vsx())       + " | ";
+
+    return s.c_str();
+}
+