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, 61 insertions, 851 deletions

diff --git a/main.cpp b/main.cpp
index fe9e583..7db3df7 100644
--- a/main.cpp
+++ b/main.cpp
@@ -1,6 +1,6 @@
-#include "ggml.h"
-
 #include "utils.h"
+#include "ggml.h"
+#include "llama.h"
 
 #include <cassert>
 #include <cinttypes>
@@ -40,7 +40,7 @@ enum console_state {
     CONSOLE_STATE_DEFAULT=0,
     CONSOLE_STATE_PROMPT,
     CONSOLE_STATE_USER_INPUT
-}; 
+};
 
 static console_state con_st = CONSOLE_STATE_DEFAULT;
 static bool con_use_color = false;
@@ -65,765 +65,6 @@ void set_console_state(console_state new_st)
     }
 }
 
-static const int EOS_TOKEN_ID = 2;
-
-// 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;
-};
-
-// load the model's weights from a file
-
-bool llama_model_load(const std::string & fname, llama_model & model, llama_vocab & vocab, int n_ctx, int n_parts, ggml_type memory_type = GGML_TYPE_F32) {
-    fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-
-    std::vector<char> f_buf(1024*1024);
-
-    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 == 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 != 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 != FILE_VERSION) {
-            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
-                    __func__, fname.c_str(), format_version, 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;
-        }
-    }
-
-    // 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();
-    }
-
-    return true;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted logits for the next token
-//
-// The GPT-J model requires about 16MB of memory per input token.
-//
-bool llama_eval(
-        const llama_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<llama_vocab::id> & embd_inp,
-              std::vector<float>           & embd_w,
-              size_t                       & mem_per_token,
-              bool return_all_logits = false) {
-    const int N = embd_inp.size();
-
-    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;
-
-    // TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
-    // static size_t buf_size = hparams.n_ctx*1024*1024;
-    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, embd_inp.data(), 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);
-
-    if (return_all_logits) {
-        embd_w.resize(n_vocab * N);
-        memcpy(embd_w.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
-    } else {
-        // return result for just the last token
-        embd_w.resize(n_vocab);
-        memcpy(embd_w.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);
-
-    return true;
-}
-
 std::vector<double> softmax(const std::vector<float>& logits) {
     std::vector<double> probs(logits.size());
     float max_logit = logits[0];
@@ -840,24 +81,25 @@ std::vector<double> softmax(const std::vector<float>& logits) {
     return probs;
 }
 
-void perplexity(const llama_vocab &vocab, const llama_model &model, const gpt_params &params, size_t mem_per_token) {
+void perplexity(llama_context * ctx, const gpt_params & params) {
     // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
     // Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
-    std::vector<llama_vocab::id> tokens = ::llama_tokenize(vocab, params.prompt, true);
+    auto tokens = ::llama_tokenize(ctx, params.prompt.c_str(), true);
 
     int count = 0;
     double nll = 0.0;
     int seq_count = tokens.size() / params.n_ctx;
-    printf("Calculating perplexity over %d chunks\n", seq_count);
+
+    fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
+
     for (int i = 0; i < seq_count; ++i) {
         int start = i * params.n_ctx;
         int end = start + params.n_ctx - 1;
-        std::vector<llama_vocab::id> embd(tokens.begin() + start, tokens.begin() + end);
-        std::vector<float> logits;
+        std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
         auto start_t = std::chrono::high_resolution_clock::now();
-        if (!llama_eval(model, params.n_threads, 0, embd, logits, mem_per_token, true)) {
-            fprintf(stderr, "Failed to predict\n");
+        if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
             return;
         }
         auto end_t = std::chrono::high_resolution_clock::now();
@@ -877,12 +119,14 @@ void perplexity(const llama_vocab &vocab, const llama_model &model, const gpt_pa
         // Example, we have a context window of 512, we will compute perplexity for each of the
         // last 256 tokens.  Then, we split the input up into context window size chunks to
         // process the entire prompt.
+
+        auto logits = llama_get_logits(ctx);
         for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
             // Calculate probability of next token, given the previous ones.
-            int n_vocab = model.hparams.n_vocab;
+            int n_vocab = llama_n_vocab(ctx);
             std::vector<float> tok_logits(
-                logits.begin() + j * n_vocab,
-                logits.begin() + (j + 1) * n_vocab);
+                logits + j * n_vocab,
+                logits + (j + 1) * n_vocab);
             double prob = softmax(tok_logits)[tokens[start + j + 1]];
             nll += -std::log(prob);
             ++count;
@@ -910,29 +154,9 @@ void sigint_handler(int signo) {
 }
 #endif
 
-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();
-}
-
 int main(int argc, char ** argv) {
+    // has to be called once at the start of the program to init ggml stuff
     ggml_time_init();
-    const int64_t t_main_start_us = ggml_time_us();
 
     gpt_params params;
     params.model = "models/llama-7B/ggml-model.bin";
@@ -964,21 +188,21 @@ int main(int argc, char ** argv) {
 //    params.prompt = R"(// this function checks if the number n is prime
 //bool is_prime(int n) {)";
 
-    int64_t t_load_us = 0;
-
-    llama_vocab vocab;
-    llama_model model;
+    llama_context * ctx;
 
     // load the model
     {
-        const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
-        const int64_t t_start_us = ggml_time_us();
-        if (!llama_model_load(params.model, model, vocab, params.n_ctx, params.n_parts, memory_type)) {
-            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+        auto lparams = llama_context_default_params();
+
+        lparams.f16_kv = params.memory_f16;
+        lparams.logits_all = params.perplexity;
+
+        ctx = llama_init_from_file(params.model.c_str(), lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
             return 1;
         }
-
-        t_load_us = ggml_time_us() - t_start_us;
     }
 
     // print system information
@@ -988,32 +212,33 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
-    std::vector<float> logits;
-
     // determine the required inference memory per token:
-    size_t mem_per_token = 0;
-    llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+    // TODO: better way to do that
+    {
+        const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
+        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
+    }
 
     if (params.perplexity) {
-        perplexity(vocab, model, params, mem_per_token);
+        perplexity(ctx, params);
         exit(0);
     }
 
     int n_past = 0;
 
-    int64_t t_sample_us  = 0;
-    int64_t t_predict_us = 0;
-
     // Add a space in front of the first character to match OG llama tokenizer behavior
     params.prompt.insert(0, 1, ' ');
+
     // tokenize the prompt
-    std::vector<llama_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
+    auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
 
-    params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
+    const int n_ctx = llama_n_ctx(ctx);
+
+    params.n_predict = std::min(params.n_predict, n_ctx - (int) embd_inp.size());
 
     // prefix & suffix for instruct mode
-    const std::vector<llama_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
-    const std::vector<llama_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
 
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
@@ -1030,7 +255,7 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
     fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
     for (int i = 0; i < (int) embd_inp.size(); i++) {
-        fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).tok.c_str());
+        fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
     }
     fprintf(stderr, "\n");
     if (params.interactive) {
@@ -1055,10 +280,10 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
     fprintf(stderr, "\n\n");
 
-    std::vector<llama_vocab::id> embd;
+    std::vector<llama_token> embd;
 
     int last_n_size = params.repeat_last_n;
-    std::vector<llama_vocab::id> last_n_tokens(last_n_size);
+    std::vector<llama_token> last_n_tokens(last_n_size);
     std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
 
     if (params.interactive) {
@@ -1092,14 +317,10 @@ int main(int argc, char ** argv) {
     while (remaining_tokens > 0 || params.interactive) {
         // predict
         if (embd.size() > 0) {
-            const int64_t t_start_us = ggml_time_us();
-
-            if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
-                fprintf(stderr, "Failed to predict\n");
+            if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
                 return 1;
             }
-
-            t_predict_us += ggml_time_us() - t_start_us;
         }
 
         n_past += embd.size();
@@ -1107,29 +328,28 @@ int main(int argc, char ** argv) {
 
         if ((int) embd_inp.size() <= input_consumed) {
             // out of user input, sample next token
-            const float top_k = params.top_k;
-            const float top_p = params.top_p;
-            const float temp  = params.temp;
+            const float top_k          = params.top_k;
+            const float top_p          = params.top_p;
+            const float temp           = params.temp;
             const float repeat_penalty = params.repeat_penalty;
 
-            const int n_vocab = model.hparams.n_vocab;
-
-            llama_vocab::id id = 0;
+            llama_token id = 0;
 
             {
-                const int64_t t_start_sample_us = ggml_time_us();
+                auto logits = llama_get_logits(ctx);
 
                 if (params.ignore_eos) {
                     // set the logit of the eos token to zero to avoid sampling it
-                    logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
+                    //logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
+                    // TODO: this does not work of params.logits_all == true
+                    assert(params.perplexity == false);
+                    logits[llama_token_eos()] = 0;
                 }
 
-                id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
+                id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
 
                 last_n_tokens.erase(last_n_tokens.begin());
                 last_n_tokens.push_back(id);
-
-                t_sample_us += ggml_time_us() - t_start_sample_us;
             }
 
             // add it to the context
@@ -1156,7 +376,7 @@ int main(int argc, char ** argv) {
         // display text
         if (!input_noecho) {
             for (auto id : embd) {
-                printf("%s", vocab.id_to_token[id].tok.c_str());
+                printf("%s", llama_token_to_str(ctx, id));
             }
             fflush(stdout);
         }
@@ -1171,7 +391,7 @@ int main(int argc, char ** argv) {
             // check for reverse prompt
             std::string last_output;
             for (auto id : last_n_tokens) {
-                last_output += vocab.id_to_token[id].tok;
+                last_output += llama_token_to_str(ctx, id);
             }
 
             // Check if each of the reverse prompts appears at the end of the output.
@@ -1208,7 +428,7 @@ int main(int argc, char ** argv) {
                 // done taking input, reset color
                 set_console_state(CONSOLE_STATE_DEFAULT);
 
-                std::vector<llama_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
+                auto line_inp = ::llama_tokenize(ctx, buffer, false);
                 embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
 
                 if (params.instruct) {
@@ -1223,7 +443,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of text token
-        if (embd.back() == EOS_TOKEN_ID) {
+        if (embd.back() == llama_token_eos()) {
             if (params.interactive) {
                 is_interacting = true;
             } else {
@@ -1243,19 +463,9 @@ int main(int argc, char ** argv) {
     signal(SIGINT, SIG_DFL);
 #endif
 
-    // report timing
-    {
-        const int64_t t_main_end_us = ggml_time_us();
-
-        fprintf(stderr, "\n\n");
-        fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
-        fprintf(stderr, "%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
-        fprintf(stderr, "%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
-        fprintf(stderr, "%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
-        fprintf(stderr, "%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
-    }
+    llama_print_timings(ctx);
 
-    ggml_free(model.ctx);
+    llama_free(ctx);
 
     set_console_state(CONSOLE_STATE_DEFAULT);