add new code

CMakeLists.txt

@@ -1,8 +1,13 @@
 cmake_minimum_required (VERSION 3.0)
 project(turbopilot VERSION 0.1.0)
 
+set(CMAKE_EXPORT_COMPILE_COMMANDS "on")
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib")
 
 add_subdirectory(extern/ggml)
 add_subdirectory(extern/argparse)
 add_subdirectory(extern/spdlog)
-add_subdirectory(src)
+add_subdirectory(src)
+
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)

include/turbopilot/gptj.hpp

@@ -0,0 +1,83 @@
+#ifndef __TURBOPILOT_GPTJ_H
+#define __TURBOPILOT_GPTJ_H
+
+#include <turbopilot/model.hpp>
+
+#include <vector>
+#include <map>
+
+// default hparams (GPT-J 6B)
+struct gptj_hparams {
+    int32_t n_vocab = 50400;
+    int32_t n_ctx   = 2048;
+    int32_t n_embd  = 4096;
+    int32_t n_head  = 16;
+    int32_t n_layer = 28;
+    int32_t n_rot   = 64;
+    int32_t ftype   = 1;
+};
+
+struct gptj_layer {
+    // normalization
+    struct ggml_tensor * ln_1_g;
+    struct ggml_tensor * ln_1_b;
+
+    // attention
+    struct ggml_tensor * c_attn_q_proj_w;
+    struct ggml_tensor * c_attn_k_proj_w;
+    struct ggml_tensor * c_attn_v_proj_w;
+
+    struct ggml_tensor * c_attn_proj_w;
+
+    // ff
+    struct ggml_tensor * c_mlp_fc_w;
+    struct ggml_tensor * c_mlp_fc_b;
+
+    struct ggml_tensor * c_mlp_proj_w;
+    struct ggml_tensor * c_mlp_proj_b;
+};
+
+struct gptj_model {
+    gptj_hparams hparams;
+
+    // normalization
+    struct ggml_tensor * ln_f_g;
+    struct ggml_tensor * ln_f_b;
+
+    struct ggml_tensor * wte; // position embedding
+
+    struct ggml_tensor * lmh_g; // language model head
+    struct ggml_tensor * lmh_b; // language model bias
+
+    std::vector<gptj_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+
+
+class GPTJModel : public TurbopilotModel {
+
+public:
+    GPTJModel(ModelConfig config, std::mt19937 &rng) : TurbopilotModel(config, rng){
+        this->model = new gptj_model{};
+        this->vocab = new gpt_vocab{};
+    }
+    virtual ~GPTJModel();
+    bool load_model(std::string path);
+    virtual std::stringstream predict(std::string prompt, int max_length);
+
+private:
+    gptj_model *model = NULL;
+    gpt_vocab *vocab = NULL;
+
+
+};
+
+#endif

include/turbopilot/model.hpp

@@ -0,0 +1,61 @@
+#ifndef __TURBOPILOT_MODEL_H
+#define __TURBOPILOT_MODEL_H
+
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <map>
+#include <vector>
+#include <random>
+
+struct gpt_vocab
+{
+    using id = int32_t;
+    using token = std::string;
+
+    std::map<token, id> token_to_id;
+    std::map<id, token> id_to_token;
+    std::vector<std::string> special_tokens;
+
+    void add_special_token(const std::string &token);
+};
+
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab &vocab, const std::string &text);
+
+gpt_vocab::id gpt_sample_top_k_top_p(
+    const gpt_vocab &vocab,
+    const float *logits,
+    int top_k,
+    double top_p,
+    double temp,
+    std::mt19937 &rng);
+
+struct ModelConfig
+{
+    int n_threads = 4;
+    int32_t top_k = 40;
+    float top_p = 0.95f;
+    float temp = 0.80f;
+    float repeat_penalty = 1.10f;
+    int32_t seed = -1;     // RNG seed
+    int32_t n_ctx = 512;   // context size
+    int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
+};
+
+class TurbopilotModel
+{
+
+public:
+    TurbopilotModel(ModelConfig config, std::mt19937 &rng) : 
+        config(config), 
+        rng(rng) 
+    {}
+    virtual bool load_model(std::string model_path) = 0;
+    virtual std::stringstream predict(std::string prompt, int max_length) = 0;
+
+protected:
+    ModelConfig config;
+    std::mt19937 &rng;
+};
+
+#endif //__TURBOPILOT_MODEL_H

src/CMakeLists.txt

@@ -0,0 +1,21 @@
+set(TURBOPILOT_TARGET turbopilot)
+
+
+
+
+add_executable(${TURBOPILOT_TARGET} 
+  main.cpp 
+  gptj.cpp
+  common.cpp
+  ../include/turbopilot/model.hpp
+  ../include/turbopilot/gptj.hpp
+  )
+
+
+target_include_directories(${TURBOPILOT_TARGET} PRIVATE
+  ../include
+  ../extern/spdlog/include
+)
+
+
+target_link_libraries(${TURBOPILOT_TARGET} PRIVATE ggml argparse)

src/common.cpp

@@ -0,0 +1,162 @@
+#include "turbopilot/model.hpp"
+
+#include <regex>
+#include <cmath>
+#include <random>
+
+
+void gpt_vocab::add_special_token(const std::string & token) {
+    special_tokens.push_back(token);
+}
+
+void gpt_split_words(std::string str, std::vector<std::string>& words) {
+    const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+    const std::regex re(pattern);
+    std::smatch m;
+
+    while (std::regex_search(str, m, re)) {
+        for (auto x : m) {
+            words.push_back(x);
+        }
+        str = m.suffix();
+    }
+}
+
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
+    std::vector<std::string> words;
+
+    // first split the text into words
+    {
+        std::string str = text;
+
+        // Generate the subpattern from the special_tokens vector if it's not empty
+        if (!vocab.special_tokens.empty()) {
+            const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
+            std::string special_tokens_subpattern;
+            for (const auto & token : vocab.special_tokens) {
+                if (!special_tokens_subpattern.empty()) {
+                    special_tokens_subpattern += "|";
+                }
+                special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
+            }
+
+            std::regex re(special_tokens_subpattern);
+            std::smatch m;
+            // Split the text by special tokens.
+            while (std::regex_search(str, m, re)) {
+                // Split the substrings in-between special tokens into words.
+                gpt_split_words(m.prefix(), words);
+                // Add matched special tokens as words.
+                for (auto x : m) {
+                    words.push_back(x);
+                }
+                str = m.suffix();
+            }
+            // Remaining text without special tokens will be handled below.
+        }
+
+        gpt_split_words(str, words);
+    }
+
+    // find the longest token that forms each word in words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        for (int i = 0; i < (int) word.size(); ){
+            for (int j = word.size() - 1; j >= i; j--){
+                auto cand = word.substr(i, j-i+1);
+                auto it = vocab.token_to_id.find(cand);
+                if (it != vocab.token_to_id.end()){ // word.substr(i, j-i+1) in vocab
+                    tokens.push_back(it->second);
+                    i = j + 1;
+                    break;
+                }
+                else if (j == i){ // word.substr(i, 1) has no matching
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
+                    i++;
+                }
+            }
+        }
+    }
+
+    return tokens;
+}
+
+gpt_vocab::id gpt_sample_top_k_top_p(
+        const gpt_vocab & vocab,
+        const float * logits,
+        int    top_k,
+        double top_p,
+        double temp,
+        std::mt19937 & rng) {
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+    logits_id.reserve(n_logits);
+
+    {
+        const double scale = 1.0/temp;
+        for (int i = 0; i < n_logits; ++i) {
+            logits_id.push_back(std::make_pair(logits[i]*scale, i));
+        }
+    }
+
+    // find the top K tokens
+    std::partial_sort(
+            logits_id.begin(),
+            logits_id.begin() + top_k, logits_id.end(),
+            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    logits_id.resize(top_k);
+
+    double maxl = -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 < top_k; i++) {
+            cumsum += probs[i];
+            if (cumsum >= top_p) {
+                top_k = i + 1;
+                probs.resize(top_k);
+                logits_id.resize(top_k);
+                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) probs.size(); i++) {
+    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
+    //}
+    //exit(0);
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}

src/gptj.cpp

@@ -0,0 +1,642 @@
+#include <turbopilot/gptj.hpp>
+#include <spdlog/spdlog.h>
+
+#include <ggml/ggml.h>
+
+#include <iostream>
+#include <fstream>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+
+// 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 gptj_eval(
+        const gptj_model & model,
+        const int n_threads,
+        const int n_past,
+        const std::vector<gpt_vocab::id> & embd_inp,
+              std::vector<float>         & embd_w,
+              size_t                     & mem_per_token) {
+    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_rot;
+
+    static size_t buf_size = 256u*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 = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
+        //printf("\n{}: 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) {
+            spdlog::error("{}: failed to allocate {} bytes\n", __func__, buf_size);
+            return false;
+        }
+    }
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf,
+        /*.no_alloc   =*/ false,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+    struct ggml_cgraph gf = {};
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
+
+    // wte
+    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * cur;
+
+        // norm
+        {
+            cur = ggml_norm(ctx0, inpL);
+
+            // cur = ln_1_g*cur + ln_1_b
+            cur = ggml_add(ctx0,
+                    ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
+                        cur),
+                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
+        }
+
+        struct ggml_tensor * inpSA = cur;
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+
+            // store key and value to memory
+            {
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur));
+
+                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_2d(ctx0, model.memory_v, N, n_embd,
+                        (   n_ctx)*ggml_element_size(model.memory_v),
+                        (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
+
+                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,
+                        Qcur,
+                        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_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),
+                        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_inplace(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_inplace(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(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 =
+                ggml_view_3d(ctx0, model.memory_v,
+                        n_past + N, n_embd/n_head, n_head,
+                        n_ctx*ggml_element_size(model.memory_v),
+                        n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
+                        il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
+
+            // KQV = transpose(V) * KQ_soft_max
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, 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].c_attn_proj_w,
+                    cur);
+        }
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        // this is independent of the self-attention result, so it could be done in parallel to the self-attention
+        {
+            // note here we pass inpSA instead of cur
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].c_mlp_fc_w,
+                    inpSA);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
+                    cur);
+
+            // GELU activation
+            cur = ggml_gelu(ctx0, cur);
+
+            // projection
+            // cur = proj_w*cur + proj_b
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].c_mlp_proj_w,
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
+                    cur);
+        }
+
+        // self-attention + FF
+        cur  = ggml_add(ctx0, cur, inpFF);
+
+        // input for next layer
+        inpL = ggml_add(ctx0, cur, inpL);
+    }
+
+    // norm
+    {
+        inpL = ggml_norm(ctx0, inpL);
+
+        // inpL = ln_f_g*inpL + ln_f_b
+        inpL = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.ln_f_g, inpL),
+                    inpL),
+                ggml_repeat(ctx0, model.ln_f_b, inpL));
+    }
+
+    // lm_head
+    {
+        inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
+
+        inpL = ggml_add(ctx0,
+                ggml_repeat(ctx0, model.lmh_b, inpL),
+                inpL);
+    }
+
+    // logits -> probs
+    //inpL = ggml_soft_max_inplace(ctx0, inpL);
+
+    // run the computation
+    ggml_build_forward_expand(&gf, inpL);
+    ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+
+    //if (n_past%100 == 0) {
+    //    ggml_graph_print   (&gf);
+    //    ggml_graph_dump_dot(&gf, NULL, "gpt-j.dot");
+    //}
+
+    //embd_w.resize(n_vocab*N);
+    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+    // 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;
+    }
+    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+
+GPTJModel::~GPTJModel(){
+    ggml_free(model->ctx);
+    free(model);
+}
+
+bool GPTJModel::load_model(std::string fname) {
+    spdlog::info("{}: loading model from '{}' - please wait ...\n", __func__, fname.c_str());
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    if (!fin) {
+        spdlog::error("{}: failed to open '{}'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic != GGML_FILE_MAGIC) {
+            spdlog::error("{}: invalid model file '{}' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+    }
+
+    // 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_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.ftype,   sizeof(hparams.ftype));
+
+        const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
+
+        spdlog::info("{}: n_vocab = {}\n", __func__, hparams.n_vocab);
+        spdlog::info("{}: n_ctx   = {}\n", __func__, hparams.n_ctx);
+        spdlog::info("{}: n_embd  = {}\n", __func__, hparams.n_embd);
+        spdlog::info("{}: n_head  = {}\n", __func__, hparams.n_head);
+        spdlog::info("{}: n_layer = {}\n", __func__, hparams.n_layer);
+        spdlog::info("{}: n_rot   = {}\n", __func__, hparams.n_rot);
+        spdlog::info("{}: ftype   = {}\n", __func__, hparams.ftype);
+        spdlog::info("{}: qntvr   = {}\n", __func__, qntvr);
+
+        hparams.ftype %= GGML_QNT_VERSION_FACTOR;
+    }
+
+    // load vocab
+    {
+        int32_t n_vocab = 0;
+        fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+        if (n_vocab != model->hparams.n_vocab) {
+            spdlog::error("{}: invalid model file '{}' (bad vocab size {} != {})\n",
+                    __func__, fname.c_str(), n_vocab, model->hparams.n_vocab);
+            return false;
+        }
+
+        std::string word;
+        std::vector<char> buf(128);
+
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            buf.resize(len);
+            fin.read((char *) buf.data(), len);
+            word.assign(buf.data(), len);
+
+            vocab->token_to_id[word] = i;
+            vocab->id_to_token[i] = word;
+        }
+    }
+
+    // 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
+    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model->hparams.ftype));
+    if (wtype == GGML_TYPE_COUNT) {
+        spdlog::error("{}: invalid model file '{}' (bad ftype value {})\n",
+                __func__, fname.c_str(), model->hparams.ftype);
+        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*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype);         // lmh_g
+        ctx_size +=        n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
+
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_v
+
+        ctx_size += (5 + 10*n_layer)*512; // object overhead
+
+        spdlog::info("{}: ggml ctx size = {} 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,
+            /*.no_alloc   =*/ false,
+        };
+
+        model->ctx = ggml_init(params);
+        if (!model->ctx) {
+            spdlog::error("{}: 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->wte    = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+
+        model->ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model->ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+        model->lmh_g  = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        model->lmh_b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
+
+        // map by name
+        model->tensors["transformer.wte.weight"] = model->wte;
+
+        model->tensors["transformer.ln_f.weight"] = model->ln_f_g;
+        model->tensors["transformer.ln_f.bias"]   = model->ln_f_b;
+
+        model->tensors["lm_head.weight"] = model->lmh_g;
+        model->tensors["lm_head.bias"]   = model->lmh_b;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model->layers[i];
+
+            layer.ln_1_g          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_1_b          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
+            layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
+            layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
+
+            layer.c_attn_proj_w   = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
+
+            layer.c_mlp_fc_w      = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
+            layer.c_mlp_fc_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
+
+            layer.c_mlp_proj_w    = ggml_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
+            layer.c_mlp_proj_b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            // map by name
+            model->tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"]          = layer.ln_1_g;
+            model->tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"]            = layer.ln_1_b;
+
+            model->tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"]   = layer.c_attn_q_proj_w;
+            model->tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"]   = layer.c_attn_k_proj_w;
+            model->tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"]   = layer.c_attn_v_proj_w;
+
+            model->tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
+
+            model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"]     = layer.c_mlp_fc_w;
+            model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"]       = layer.c_mlp_fc_b;
+
+            model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"]    = layer.c_mlp_proj_w;
+            model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"]      = layer.c_mlp_proj_b;
+        }
+    }
+
+    // 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, GGML_TYPE_F16, n_elements);
+        model->memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model->memory_k) + ggml_nbytes(model->memory_v);
+
+        spdlog::info("{}: memory_size = {} MB, n_mem = {}\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    // load weights
+    {
+        int n_tensors = 0;
+        size_t total_size = 0;
+
+        spdlog::info("{}: ", __func__);
+
+        while (true) {
+            int32_t n_dims;
+            int32_t length;
+            int32_t ttype;
+
+            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+            fin.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
+
+            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()) {
+                spdlog::error("{}: unknown tensor '{}' in model file\n", __func__, name.data());
+                return false;
+            }
+
+            auto tensor = model->tensors[name.data()];
+            if (ggml_nelements(tensor) != nelements) {
+                spdlog::error("{}: tensor '{}' has wrong size in model file\n", __func__, name.data());
+                return false;
+            }
+
+            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                spdlog::error("{}: tensor '{}' has wrong shape in model file: got [{}, {}], expected [{}, {}]\n",
+                        __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
+                return false;
+            }
+
+            // for debugging
+            if (0) {
+                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
+            }
+
+            const size_t bpe = ggml_type_size(ggml_type(ttype));
+
+            if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+                spdlog::error("{}: tensor '{}' has wrong size in model file: got %zu, expected %zu\n",
+                        __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                return false;
+            }
+
+            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+            //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ttype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+            total_size += ggml_nbytes(tensor);
+            if (++n_tensors % 8 == 0) {
+                printf(".");
+                fflush(stdout);
+            }
+            
+        }
+
+        printf("\n");
+        spdlog::info(" done\n");
+
+        spdlog::info("{}: model size = {:06.2f} MB / num tensors = {}\n", __func__, total_size/1024.0/1024.0, n_tensors);
+    }
+
+    fin.close();
+
+    return true;
+}
+
+std::stringstream GPTJModel::predict(std::string prompt, int max_length) {
+
+    std::stringstream result;
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize((*vocab), prompt);
+
+    int n_past = 0;
+
+    int64_t t_sample_us  = 0;
+    int64_t t_predict_us = 0;
+
+    int n_predict = std::min(max_length, model->hparams.n_ctx - (int) embd_inp.size());
+
+    spdlog::debug("{}: number of tokens in prompt = {}", __func__, embd_inp.size());
+
+    std::vector<gpt_vocab::id> embd;
+
+    // determine the required inference memory per token:
+    size_t mem_per_token = 0;
+
+    std::vector<float> logits;
+
+    gptj_eval((*model), config.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+
+    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
+        // predict
+        if (embd.size() > 0) {
+            const int64_t t_start_us = ggml_time_us();
+
+            if (!gptj_eval((*model), config.n_threads, n_past, embd, logits, mem_per_token)) {
+                throw std::runtime_error("Failed to predict");
+            }
+
+            t_predict_us += ggml_time_us() - t_start_us;
+        }
+
+        n_past += embd.size();
+        embd.clear();
+
+        if (i >= embd_inp.size()) {
+            // sample next token
+            const int   top_k = config.top_k;
+            const float top_p = config.top_p;
+            const float temp  = config.temp;
+
+            const int n_vocab = model->hparams.n_vocab;
+
+            gpt_vocab::id id = 0;
+
+            {
+                const int64_t t_start_sample_us = ggml_time_us();
+
+                id = gpt_sample_top_k_top_p((*vocab), logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
+
+                t_sample_us += ggml_time_us() - t_start_sample_us;
+            }
+
+            // add it to the context
+            embd.push_back(id);
+        } else {
+            // if here, it means we are still processing the input prompt
+            for (int k = i; k < embd_inp.size(); k++) {
+                embd.push_back(embd_inp[k]);
+                if (embd.size() > config.n_batch) {
+                    break;
+                }
+            }
+            i += embd.size() - 1;
+        }
+
+        // display text
+        for (auto id : embd) {
+            result << vocab->id_to_token[id].c_str();
+            //printf("%s", vocab->id_to_token[id].c_str());
+        }
+        fflush(stdout);
+
+        // end of text token
+        if (embd.back() == 50256) {
+            break;
+        }
+    }
+
+    return result;
+}

src/main.cpp

@@ -0,0 +1,84 @@
+#include <iostream>
+#include <cstdio>
+#include <ggml/ggml.h>
+
+#include <spdlog/spdlog.h>
+
+#include <argparse/argparse.hpp>
+#include "turbopilot/model.hpp"
+
+#include "turbopilot/gptj.hpp"
+
+int main(int argc, char **argv)
+{
+
+    argparse::ArgumentParser program("turbopilot");
+
+    program.add_argument("-f", "--model-file")
+        .help("Path to the model that turbopilot should serve")
+        .required();
+
+    program.add_argument("-t", "--model-type")
+        .help("The type of model to load. Can be codegen/gpt-j or starcoder architectures.")
+        .default_value("codegen");
+
+    program.add_argument("-p", "--port")
+        .help("The tcp port that turbopilot should listen on")
+        .default_value("18080");
+
+    program.add_argument("-r", "--random-seed")
+        .help("Set the random seed for RNG functions")
+        .default_value(-1)
+        .scan<'i', int>();
+
+
+    try
+    {
+        program.parse_args(argc, argv);
+    }
+    catch (const std::runtime_error &err)
+    {
+        std::cerr << err.what() << std::endl;
+        std::cerr << program;
+        return 1;
+    }
+
+    ggml_time_init();
+
+    const int64_t t_main_start_us = ggml_time_us();
+
+
+    TurbopilotModel *model = NULL;
+
+    auto model_type = program.get<std::string>("--model-type");
+
+    ModelConfig config{};
+    std::mt19937 rng(program.get<int>("--random-seed"));
+
+    if(model_type.compare("codegen") == 0) {
+        spdlog::info("Initializing GPT-J type model for '{}' model", model_type);
+        model = new GPTJModel(config, rng);
+    }else{
+        spdlog::error("Invalid model type: {}", model_type);
+    }
+
+    spdlog::info("Attempt to load model from {}", program.get<std::string>("--model-type"));
+    int64_t t_load_us = 0;
+    const int64_t t_start_us = ggml_time_us();
+    auto loaded = model->load_model(program.get<std::string>("--model-file"));
+
+    if(!loaded){
+        spdlog::error("Failed to load model");
+        return -1;
+    }
+
+    t_load_us = ggml_time_us() - t_start_us;
+
+    spdlog::info("Loaded model in {:0.2f}ms", t_load_us/1000.0f);
+
+    auto result = model->predict("test", 100);
+
+    spdlog::info("output: {}", result.str());
+
+    free(model);
+}