Felipe97/llama-cpp-compiled

1#pragma once2 3#include "llama.h"4 5#include "llama-impl.h"6#include "llama-arch.h"7#include "llama-hparams.h"8#include "llama-mmap.h"9 10#include "ggml-cpp.h"11 12#include <cstddef>13#include <cstring>14#include <map>15#include <set>16#include <stdexcept>17#include <unordered_map>18 19using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;20 21// lists of buffer types used for each layer22using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;23 24enum llama_fver {25    GGUF_FILE_VERSION_V1 = 1,26    GGUF_FILE_VERSION_V2 = 2,27    GGUF_FILE_VERSION_V3 = 3,28};29 30const char * llama_file_version_name(llama_fver version);31 32struct llama_model_loader {33    // Holds information on a model weight34    struct llama_tensor_weight {35        uint16_t  idx; // source file index36        size_t   offs; // tensor data offset in the original file37 38        ggml_tensor * tensor;39 40        llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {41            const int tensor_idx = gguf_find_tensor(gguf_ctx,  ggml_get_name(tensor));42            if (tensor_idx < 0) {43                throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor)));44            }45 46            offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);47            if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size()) {48                throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor)));49            }50        }51    };52 53    // custom comparator to sort weights more nicely by layer54    struct weight_name_comparer {55        bool operator()(const std::string & a, const std::string & b) const {56            int a_layer = -1;57            int b_layer = -1;58            sscanf(a.c_str(), "blk.%d.", &a_layer);59            sscanf(b.c_str(), "blk.%d.", &b_layer);60            if (a_layer != b_layer) {61                return a_layer < b_layer;62            }63            return a < b;64        }65    };66 67    static const int TENSOR_NOT_REQUIRED    = 1 << 0;68    static const int TENSOR_DUPLICATED      = 1 << 1;69    static const int TENSOR_SKIP            = 1 << 2;70    static const int TENSOR_SKIP_IF_VIRTUAL = 1 << 3;71    static const int TENSOR_ALLOW_RESHAPE   = 1 << 4;72    static const int TENSOR_READ_LAZY       = 1 << 5; // read rows on demand instead of loading whole tensor; requires mmap for now73 74    int n_kv      = 0;75    int n_tensors = 0;76    int n_created = 0;77 78    uint64_t n_elements = 0;79    size_t   n_bytes    = 0;80 81    bool use_mmap = false;82    bool use_direct_io = false;83    bool check_tensors;84    bool no_alloc;85    bool load_mtp;86 87    // handle TENSOR_READ_LAZY88    // use case: keep PLE / engrams embd tensors on disk, read them on demand89    struct lazy_read {90        // set by the caller before the create_tensor() calls91        enum llama_lazy_mode mode = LLAMA_LAZY_MODE_OFF;92 93        // decide whether this tensor is read lazily94        // pass w to also record it, or nullptr to only ask95        bool add(const std::string & name, const ggml_tensor * t, const llama_tensor_weight * w);96 97        bool any() const {98            return !ranges.empty();99        }100 101        bool has(const ggml_tensor * t) const {102            return tensors.count(ggml_get_name(t)) > 0;103        }104 105        const llama_mmap::ranges & for_file(uint32_t idx) const {106            static const llama_mmap::ranges none;107 108            const auto it = ranges.find(idx);109            return it == ranges.end() ? none : it->second;110        }111 112        // lazy tensors are gathered on the host, so no offload setting applies to them113        static ggml_backend_buffer_type_t buft();114 115    private:116        std::map<uint32_t, llama_mmap::ranges> ranges;117        std::set<std::string>                  tensors;118    } lazy;119 120    llama_files files;121    llama_ftype ftype;122    llama_fver  fver;123 124    llama_mmaps mappings;125 126    std::map<std::string, llama_tensor_weight, weight_name_comparer> weights_map;127    std::unordered_map<std::string, llama_model_kv_override> kv_overrides;128    const llama_model_tensor_buft_override * tensor_buft_overrides;129 130    gguf_context_ptr metadata_ptr;131    struct gguf_context * metadata; // either metadata_ptr.get() or externally set132    llama_model_set_tensor_data_t set_tensor_data;133    void * set_tensor_data_ud;134    std::vector<ggml_context_ptr> contexts;135 136    std::string arch_name;137    LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);138 139    size_t size_done = 0;140    size_t size_data = 0;141    std::vector<std::pair<size_t, size_t>> mmaps_used;142 143    // define a comparator for the buft -> ctx map to ensure that the order is well-defined:144    struct ggml_backend_buft_comparator {145        bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const {146            return strcmp(ggml_backend_buft_name(lhs), ggml_backend_buft_name(rhs)) < 0;147        }148    };149 150    // lazy tensors need dedicated context151    struct ctx_key {152        ggml_backend_buffer_type_t buft;153        bool lazy;154    };155 156    struct ctx_key_comparator {157        bool operator()(const ctx_key & lhs, const ctx_key & rhs) const {158            if (lhs.lazy != rhs.lazy) {159                return lhs.lazy < rhs.lazy;160            }161            return strcmp(ggml_backend_buft_name(lhs.buft), ggml_backend_buft_name(rhs.buft)) < 0;162        }163    };164 165    std::map<ctx_key, ggml_context_ptr, ctx_key_comparator> ctx_map;166 167    // track tensors that had to be moved for debugging:168    size_t n_tensors_moved = 0;169    std::string first_tensor_moved_name;170    std::string first_tensor_moved_type_name;171    ggml_backend_buffer_type_t first_moved_from_buft = nullptr;172    ggml_backend_buffer_type_t first_moved_to_buft = nullptr;173 174    llama_model_loader(175        struct gguf_context * metadata,176        llama_model_set_tensor_data_t set_tensor_data,177        void * set_tensor_data_ud,178        const std::string & fname,179        std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme180        FILE * file,181        llama_load_mode load_mode,182        bool check_tensors,183        bool no_alloc,184        bool load_mtp,185        const llama_model_kv_override * param_overrides_p,186        const llama_model_tensor_buft_override * param_tensor_buft_overrides_p);187 188    template<typename T>189    typename std::enable_if<std::is_integral<T>::value, bool>::type190    get_arr_n(const std::string & key, T & result, bool required = true);191 192    template<typename T>193    typename std::enable_if<std::is_integral<T>::value, bool>::type194    get_arr_n(enum llm_kv kid, T & result, bool required = true);195 196    template<typename T>197    bool get_arr(const std::string & key, std::vector<T> & result, bool required = true);198 199    template<typename T, size_t N_MAX>200    bool get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required = true);201 202    template<typename T>203    bool get_arr(enum llm_kv kid, T & result, bool required = true);204 205    template<typename T>206    bool get_key(const std::string & key, T & result, bool required = true);207 208    template<typename T>209    bool get_key(enum llm_kv kid, T & result, bool required = true);210 211    template<typename T, size_t N_MAX>212    bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, bool required = true);213 214    template<typename T>215    bool get_key_or_arr(enum llm_kv kid, T & result, uint32_t n, bool required = true);216 217    bool get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required = true);218 219    std::string get_arch_name() const;220 221    enum llm_arch get_arch() const;222 223    const llama_tensor_weight * get_weight(const char * name) const;224 225    const llama_tensor_weight & require_weight(const char * name) const;226 227    struct ggml_tensor * get_tensor_meta(const char * name) const;228 229    struct ggml_tensor * require_tensor_meta(const std::string & name) const;230 231    const struct ggml_tensor * check_tensor_dims(232            const std::string & name,233            const std::vector<int64_t> & ne,234            bool required,235            bool allow_reshape) const;236 237    struct ggml_tensor * create_tensor(238        const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output,239        const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);240 241    void done_getting_tensors(bool partial = false) const;242 243    void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);244 245    void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const;246 247    // release a weight's mmap pages248    void unmap_weight(const llama_tensor_weight & w) const;249 250    // read a byte range of a weight's data251    // with mmap, returns a pointer into the mapping, otherwise reads into buf and returns buf252    const void * load_data_range(const llama_tensor_weight & w, size_t offs, size_t size, void * buf) const;253 254    // Returns false if cancelled by progress_callback255    bool load_all_data(256            struct ggml_context * ctx,257            llama_buf_map & bufs,258            llama_mlocks * lmlocks,259            llama_progress_callback progress_callback,260            void * progress_callback_user_data);261 262    std::string ftype_name() const;263 264    void print_info() const;265};266