--- /dev/null
+// This file contains functionality related to "GGUF" files, the binary file format used by ggml.
+// GGUF files have the following structure:
+//
+// 1. File magic "GGUF" (4 bytes).
+// 2. File version (uint32_t).
+// 3. Number of ggml tensors in file (int64_t).
+// 4. Number of key-value-pairs in file (int64_t).
+// 5. For each KV pair:
+// 1. The key (string).
+// 2. The value type (gguf_type).
+// 3a. If the value type is GGUF_TYPE_ARRAY:
+// 1. The type of the array (gguf_type).
+// 2. The number of elements in the array (uint64_t).
+// 3. The binary representation of each element in the array.
+// 3b. Otherwise:
+// 1. The binary representation of the value.
+// 6. For each ggml tensor:
+// 1. The tensor name (string).
+// 2. The number of dimensions of the tensor (uint32_t).
+// 3. For each dimension:
+// 1. The size of the tensor in the dimension (int64_t).
+// 4. The tensor data type (ggml_type).
+// 5. The tensor data offset in the tensor data binary blob (uint64_t).
+// 7. The tensor data binary blob (optional, aligned).
+//
+// Strings are serialized as the string length (uint64_t) followed by the C string without the null terminator.
+// All enums are stored as int32_t.
+// All bool values are stored as int8_t.
+// If the special key "general.alignment" (uint32_t) is defined it is used for alignment,
+// otherwise GGUF_DEFAULT_ALIGNMENT is used.
+//
+// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
+
+#pragma once
+
+#include "ggml.h"
+
+#include <stdbool.h>
+#include <stdint.h>
+
+#define GGUF_MAGIC "GGUF"
+#define GGUF_VERSION 3
+
+#define GGUF_KEY_GENERAL_ALIGNMENT "general.alignment"
+
+#define GGUF_DEFAULT_ALIGNMENT 32
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+ // types that can be stored as GGUF KV data
+ enum gguf_type {
+ GGUF_TYPE_UINT8 = 0,
+ GGUF_TYPE_INT8 = 1,
+ GGUF_TYPE_UINT16 = 2,
+ GGUF_TYPE_INT16 = 3,
+ GGUF_TYPE_UINT32 = 4,
+ GGUF_TYPE_INT32 = 5,
+ GGUF_TYPE_FLOAT32 = 6,
+ GGUF_TYPE_BOOL = 7,
+ GGUF_TYPE_STRING = 8,
+ GGUF_TYPE_ARRAY = 9,
+ GGUF_TYPE_UINT64 = 10,
+ GGUF_TYPE_INT64 = 11,
+ GGUF_TYPE_FLOAT64 = 12,
+ GGUF_TYPE_COUNT, // marks the end of the enum
+ };
+
+ struct gguf_context;
+
+ struct gguf_init_params {
+ bool no_alloc;
+
+ // if not NULL, create a ggml_context and allocate the tensor data in it
+ struct ggml_context ** ctx;
+ };
+
+ GGML_API struct gguf_context * gguf_init_empty(void);
+ GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
+ //GGML_API struct gguf_context * gguf_init_from_buffer(..);
+
+ GGML_API void gguf_free(struct gguf_context * ctx);
+
+ GGML_API const char * gguf_type_name(enum gguf_type type);
+
+ GGML_API uint32_t gguf_get_version (const struct gguf_context * ctx);
+ GGML_API size_t gguf_get_alignment (const struct gguf_context * ctx);
+ GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
+
+ GGML_API int64_t gguf_get_n_kv(const struct gguf_context * ctx);
+ GGML_API int64_t gguf_find_key(const struct gguf_context * ctx, const char * key); // returns -1 if key is not found
+ GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int64_t key_id);
+
+ GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id);
+
+ // will abort if the wrong type is used for the key
+ GGML_API uint8_t gguf_get_val_u8 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API int8_t gguf_get_val_i8 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API uint16_t gguf_get_val_u16 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API int16_t gguf_get_val_i16 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API uint32_t gguf_get_val_u32 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API int32_t gguf_get_val_i32 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API float gguf_get_val_f32 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API uint64_t gguf_get_val_u64 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API int64_t gguf_get_val_i64 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id);
+ GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int64_t key_id);
+ GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id);
+ GGML_API size_t gguf_get_arr_n (const struct gguf_context * ctx, int64_t key_id);
+
+ // get raw pointer to the first element of the array with the given key_id
+ // for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
+ GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
+
+ // get ith C string from array with given key_id
+ GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);
+
+ GGML_API int64_t gguf_get_n_tensors (const struct gguf_context * ctx);
+ GGML_API int64_t gguf_find_tensor (const struct gguf_context * ctx, const char * name); // returns -1 if the tensor is not found
+ GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id);
+ GGML_API const char * gguf_get_tensor_name (const struct gguf_context * ctx, int64_t tensor_id);
+ GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int64_t tensor_id);
+ GGML_API size_t gguf_get_tensor_size (const struct gguf_context * ctx, int64_t tensor_id);
+
+ // removes key if it exists, returns id that the key had prior to removal (-1 if it didn't exist)
+ GGML_API int64_t gguf_remove_key(struct gguf_context * ctx, const char * key);
+
+ // overrides an existing KV pair or adds a new one, the new KV pair is always at the back
+ GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
+ GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
+ GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
+ GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
+ GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
+ GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
+ GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
+ GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
+ GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
+ GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
+ GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
+ GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
+
+ // creates a new array with n elements of the given type and copies the corresponding number of bytes from data
+ GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n);
+
+ // creates a new array with n strings and copies the corresponding strings from data
+ GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, size_t n);
+
+ // set or add KV pairs from another context
+ GGML_API void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src);
+
+ // add tensor to GGUF context, tensor name must be unique
+ GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
+
+ // after changing a tensor's type, the offsets of all tensors with higher indices are immediately recalculated
+ // in such a way that the tensor data remains as one contiguous block (except for padding)
+ GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
+
+ // assumes that at least gguf_get_tensor_size bytes can be read from data
+ GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data);
+
+ // writing gguf files can be done in 3 ways:
+ //
+ // - write the entire gguf_context to a binary file in a single pass:
+ //
+ // gguf_write_to_file(ctx, fname, /*only_meta =*/ false);
+ //
+ // - write only the meta data to a file, then re-open the file and append the tensor data:
+ //
+ // gguf_write_to_file(ctx, fname, /*only_meta =*/ true);
+ // FILE * f = fopen(fname, "ab");
+ // fwrite(f, ...); // write tensor data
+ // fclose(f);
+ //
+ // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
+ //
+ // FILE * f = fopen(fname, "wb");
+ // const size_t size_meta = gguf_get_meta_size(ctx);
+ // fseek(f, size_meta, SEEK_SET);
+ // fwrite(f, ...); // write tensor data
+ // void * data = malloc(size_meta);
+ // gguf_get_meta_data(ctx, data);
+ // rewind(f);
+ // fwrite(data, 1, data, f);
+ // free(data);
+ // fclose(f);
+ //
+
+ // write the entire context to a binary file
+ GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
+
+ // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
+ GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
+
+ // writes the meta data to pointer "data"
+ GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data);
+
+#ifdef __cplusplus
+}
+#endif
--- /dev/null
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "ggml-impl.h"
+#include "gguf.h"
+
+#include <cinttypes>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <map>
+#include <new>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+template <typename T>
+struct type_to_gguf_type;
+
+template <>
+struct type_to_gguf_type<uint8_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_UINT8;
+};
+
+template <>
+struct type_to_gguf_type<int8_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_INT8;
+};
+
+template <>
+struct type_to_gguf_type<uint16_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_UINT16;
+};
+
+template <>
+struct type_to_gguf_type<int16_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_INT16;
+};
+
+template <>
+struct type_to_gguf_type<uint32_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_UINT32;
+};
+
+template <>
+struct type_to_gguf_type<int32_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_INT32;
+};
+
+template <>
+struct type_to_gguf_type<float> {
+ static constexpr enum gguf_type value = GGUF_TYPE_FLOAT32;
+};
+
+template <>
+struct type_to_gguf_type<bool> {
+ static constexpr enum gguf_type value = GGUF_TYPE_BOOL;
+};
+
+template <>
+struct type_to_gguf_type<std::string> {
+ static constexpr enum gguf_type value = GGUF_TYPE_STRING;
+};
+
+template <>
+struct type_to_gguf_type<uint64_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_UINT64;
+};
+
+template <>
+struct type_to_gguf_type<int64_t> {
+ static constexpr enum gguf_type value = GGUF_TYPE_INT64;
+};
+
+template <>
+struct type_to_gguf_type<double> {
+ static constexpr enum gguf_type value = GGUF_TYPE_FLOAT64;
+};
+
+static const std::map<gguf_type, size_t> GGUF_TYPE_SIZE = {
+ {GGUF_TYPE_UINT8, sizeof(uint8_t)},
+ {GGUF_TYPE_INT8, sizeof(int8_t)},
+ {GGUF_TYPE_UINT16, sizeof(uint16_t)},
+ {GGUF_TYPE_INT16, sizeof(int16_t)},
+ {GGUF_TYPE_UINT32, sizeof(uint32_t)},
+ {GGUF_TYPE_INT32, sizeof(int32_t)},
+ {GGUF_TYPE_FLOAT32, sizeof(float)},
+ {GGUF_TYPE_BOOL, sizeof(int8_t)},
+ {GGUF_TYPE_STRING, 0}, // undefined
+ {GGUF_TYPE_ARRAY, 0}, // undefined
+ {GGUF_TYPE_UINT64, sizeof(uint64_t)},
+ {GGUF_TYPE_INT64, sizeof(int64_t)},
+ {GGUF_TYPE_FLOAT64, sizeof(double)},
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+static const std::map<gguf_type, const char *> GGUF_TYPE_NAME = {
+ {GGUF_TYPE_UINT8, "u8"},
+ {GGUF_TYPE_INT8, "i8"},
+ {GGUF_TYPE_UINT16, "u16"},
+ {GGUF_TYPE_INT16, "i16"},
+ {GGUF_TYPE_UINT32, "u32"},
+ {GGUF_TYPE_INT32, "i32"},
+ {GGUF_TYPE_FLOAT32, "f32"},
+ {GGUF_TYPE_BOOL, "bool"},
+ {GGUF_TYPE_STRING, "str"},
+ {GGUF_TYPE_ARRAY, "arr"},
+ {GGUF_TYPE_UINT64, "u64"},
+ {GGUF_TYPE_INT64, "i64"},
+ {GGUF_TYPE_FLOAT64, "f64"},
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+size_t gguf_type_size(enum gguf_type type) {
+ auto it = GGUF_TYPE_SIZE.find(type);
+ return it == GGUF_TYPE_SIZE.end() ? 0 : it->second;
+}
+
+struct gguf_kv {
+ std::string key;
+
+ bool is_array;
+ enum gguf_type type;
+
+ std::vector<int8_t> data;
+ std::vector<std::string> data_string;
+
+ template <typename T>
+ gguf_kv(const std::string & key, const T value)
+ : key(key), is_array(false), type(type_to_gguf_type<T>::value) {
+ GGML_ASSERT(!key.empty());
+ data.resize(sizeof(T));
+ memcpy(data.data(), &value, sizeof(T));
+ }
+
+ template <typename T>
+ gguf_kv(const std::string & key, const std::vector<T> & value)
+ : key(key), is_array(true), type(type_to_gguf_type<T>::value) {
+ GGML_ASSERT(!key.empty());
+ data.resize(value.size()*sizeof(T));
+ for (size_t i = 0; i < value.size(); ++i) {
+ const T tmp = value[i];
+ memcpy(data.data() + i*sizeof(T), &tmp, sizeof(T));
+ }
+ }
+
+ gguf_kv(const std::string & key, const std::string & value)
+ : key(key), is_array(false), type(GGUF_TYPE_STRING) {
+ GGML_ASSERT(!key.empty());
+ data_string.push_back(value);
+ }
+
+ gguf_kv(const std::string & key, const std::vector<std::string> & value)
+ : key(key), is_array(true), type(GGUF_TYPE_STRING) {
+ GGML_ASSERT(!key.empty());
+ data_string = value;
+ }
+
+ const std::string & get_key() const {
+ return key;
+ }
+
+ const enum gguf_type & get_type() const {
+ return type;
+ }
+
+ size_t get_ne() const {
+ if (type == GGUF_TYPE_STRING) {
+ const size_t ne = data_string.size();
+ GGML_ASSERT(is_array || ne == 1);
+ return ne;
+ }
+ const size_t type_size = gguf_type_size(type);
+ GGML_ASSERT(data.size() % type_size == 0);
+ const size_t ne = data.size() / type_size;
+ GGML_ASSERT(is_array || ne == 1);
+ return ne;
+ }
+
+ template <typename T>
+ const T & get_val(const size_t i = 0) const {
+ GGML_ASSERT(type_to_gguf_type<T>::value == type);
+ if constexpr (std::is_same<T, std::string>::value) {
+ GGML_ASSERT(data_string.size() >= i+1);
+ return data_string[i];
+ }
+ const size_t type_size = gguf_type_size(type);
+ GGML_ASSERT(data.size() % type_size == 0);
+ GGML_ASSERT(data.size() >= (i+1)*type_size);
+ return reinterpret_cast<const T *>(data.data())[i];
+ }
+
+ void cast(const enum gguf_type new_type) {
+ const size_t new_type_size = gguf_type_size(new_type);
+ GGML_ASSERT(data.size() % new_type_size == 0);
+ type = new_type;
+ }
+};
+
+struct gguf_tensor_info {
+ struct ggml_tensor t; // for holding the equivalent info
+ uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT`
+};
+
+struct gguf_context {
+ uint32_t version = GGUF_VERSION;
+
+ std::vector<struct gguf_kv> kv;
+ std::vector<struct gguf_tensor_info> info;
+
+ size_t alignment = GGUF_DEFAULT_ALIGNMENT;
+ size_t offset = 0; // offset of `data` from beginning of file
+ size_t size = 0; // size of `data` in bytes
+
+ void * data = nullptr;
+};
+
+struct gguf_reader {
+ FILE * file;
+
+ gguf_reader(FILE * file) : file(file) {}
+
+ template <typename T>
+ bool read(T & dst) const {
+ return fread(&dst, 1, sizeof(dst), file) == sizeof(dst);
+ }
+
+ template <typename T>
+ bool read(std::vector<T> & dst, const size_t n) const {
+ dst.resize(n);
+ for (size_t i = 0; i < dst.size(); ++i) {
+ if constexpr (std::is_same<T, bool>::value) {
+ bool tmp;
+ if (!read(tmp)) {
+ return false;
+ }
+ dst[i] = tmp;
+ } else {
+ if (!read(dst[i])) {
+ return false;
+ }
+ }
+ }
+ return true;
+ }
+
+ bool read(bool & dst) const {
+ int8_t tmp = -1;
+ if (!read(tmp)) {
+ return false;
+ }
+ dst = tmp != 0;
+ return true;
+ }
+
+ bool read(enum ggml_type & dst) const {
+ int32_t tmp = -1;
+ if (!read(tmp)) {
+ return false;
+ }
+ dst = ggml_type(tmp);
+ return true;
+ }
+
+ bool read(enum gguf_type & dst) const {
+ int32_t tmp = -1;
+ if (!read(tmp)) {
+ return false;
+ }
+ dst = gguf_type(tmp);
+ return true;
+ }
+
+ bool read(std::string & dst) const {
+ uint64_t size = -1;
+ if (!read(size)) {
+ return false;
+ }
+ dst.resize(size);
+ return fread(dst.data(), 1, dst.length(), file) == dst.length();
+ }
+
+ bool read(void * dst, const size_t size) const {
+ return fread(dst, 1, size, file) == size;
+ }
+};
+
+struct gguf_context * gguf_init_empty(void) {
+ return new gguf_context;
+}
+
+template<typename T>
+bool gguf_read_emplace_helper(const struct gguf_reader & gr, std::vector<struct gguf_kv> & kv, const std::string & key, const bool is_array, const size_t n) {
+ if (is_array) {
+ std::vector<T> value;
+ try {
+ if (!gr.read(value, n)) {
+ return false;
+ }
+ } catch (std::length_error &) {
+ fprintf(stderr, "%s: encountered length_error while reading value for key '%s'\n", __func__, key.c_str());
+ return false;
+ } catch (std::bad_alloc &) {
+ fprintf(stderr, "%s: encountered bad_alloc error while reading value for key '%s'\n", __func__, key.c_str());
+ return false;
+ }
+ kv.emplace_back(key, value);
+ } else {
+ T value;
+ if (!gr.read(value)) {
+ return false;
+ }
+ kv.emplace_back(key, value);
+ }
+ return true;
+}
+
+struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
+ const struct gguf_reader gr(file);
+ struct gguf_context * ctx = new gguf_context;
+
+ bool ok = true;
+
+ // file magic
+ {
+ std::vector<char> magic;
+ ok = ok && gr.read(magic, 4);
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to read magic\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ for (uint32_t i = 0; i < magic.size(); i++) {
+ if (magic[i] != GGUF_MAGIC[i]) {
+ fprintf(stderr, "%s: invalid magic characters: '%c%c%c%c', expected 'GGUF'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
+ gguf_free(ctx);
+ return nullptr;
+ }
+ }
+ }
+
+ // header
+ int64_t n_kv = 0;
+ int64_t n_tensors = 0;
+
+ if (ok && gr.read(ctx->version)) {
+ if (ctx->version == 1) {
+ fprintf(stderr, "%s: GGUFv1 is no longer supported, please use a more up-to-date version\n", __func__);
+ ok = false;
+ }
+ if (ctx->version > GGUF_VERSION) {
+ fprintf(stderr, "%s: this GGUF file is version %" PRIu32 " but this software only supports up to version %d\n",
+ __func__, ctx->version, GGUF_VERSION);
+ ok = false;
+ }
+ } else {
+ ok = false;
+ }
+
+ if (ok && gr.read(n_tensors)) {
+ static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing");
+ if (n_tensors < 0 || n_tensors > int64_t(SIZE_MAX/sizeof(gguf_tensor_info))) {
+ fprintf(stderr, "%s: number of tensors is %" PRIi64 " but must be in [0, %zu]\n",
+ __func__, n_tensors, SIZE_MAX/sizeof(gguf_tensor_info));
+ ok = false;
+ }
+ } else {
+ ok = false;
+ }
+
+ if (ok && gr.read(n_kv)) {
+ static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing");
+ if (n_kv < 0 || n_kv > int64_t(SIZE_MAX/sizeof(gguf_kv))) {
+ fprintf(stderr, "%s: number of key value pairs is %" PRIi64 " but must be in [0, %zu]\n",
+ __func__, n_kv, SIZE_MAX/sizeof(gguf_kv));
+ ok = false;
+ }
+ } else {
+ ok = false;
+ }
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to read header\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ // KV pairs
+ {
+ for (int64_t i = 0; ok && i < n_kv; ++i) {
+ std::string key;
+ gguf_type type = gguf_type(-1);
+ bool is_array = false;
+ uint64_t n = 1;
+
+ try {
+ ok = ok && gr.read(key);
+ } catch (std::length_error &) {
+ fprintf(stderr, "%s: encountered length_error while reading key %" PRIi64 "\n", __func__, i);
+ ok = false;
+ } catch (std::bad_alloc &) {
+ fprintf(stderr, "%s: encountered bad_alloc error while reading key %" PRIi64 "\n", __func__, i);
+ ok = false;
+ }
+ for (size_t j = 0; ok && j < ctx->kv.size(); ++j) {
+ if (key == ctx->kv[j].key) {
+ fprintf(stderr, "%s: duplicate key '%s' for tensors %zu and %" PRIi64 " \n", __func__, key.c_str(), j, i);
+ ok = false;
+ }
+ }
+ if (!ok) {
+ break;
+ }
+
+ ok = ok && gr.read(type);
+ if (type == GGUF_TYPE_ARRAY) {
+ is_array = true;
+ ok = ok && gr.read(type);
+ ok = ok && gr.read(n);
+ }
+ if (!ok) {
+ break;
+ }
+
+ switch (type) {
+ case GGUF_TYPE_UINT8: ok = ok && gguf_read_emplace_helper<uint8_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_INT8: ok = ok && gguf_read_emplace_helper<int8_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_UINT16: ok = ok && gguf_read_emplace_helper<uint16_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_INT16: ok = ok && gguf_read_emplace_helper<int16_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_UINT32: ok = ok && gguf_read_emplace_helper<uint32_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_INT32: ok = ok && gguf_read_emplace_helper<int32_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_FLOAT32: ok = ok && gguf_read_emplace_helper<float> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_BOOL: ok = ok && gguf_read_emplace_helper<bool> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_STRING: ok = ok && gguf_read_emplace_helper<std::string>(gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_UINT64: ok = ok && gguf_read_emplace_helper<uint64_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_INT64: ok = ok && gguf_read_emplace_helper<int64_t> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_FLOAT64: ok = ok && gguf_read_emplace_helper<double> (gr, ctx->kv, key, is_array, n); break;
+ case GGUF_TYPE_ARRAY:
+ default:
+ {
+ fprintf(stderr, "%s: key '%s' has invalid GGUF type %d\n", __func__, key.c_str(), type);
+ ok = false;
+ } break;
+ }
+ }
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+ GGML_ASSERT(int64_t(ctx->kv.size()) == n_kv);
+
+ const int alignment_idx = gguf_find_key(ctx, GGUF_KEY_GENERAL_ALIGNMENT);
+ ctx->alignment = alignment_idx == -1 ? GGUF_DEFAULT_ALIGNMENT : gguf_get_val_u32(ctx, alignment_idx);
+
+ if (ctx->alignment == 0 || (ctx->alignment & (ctx->alignment - 1)) != 0) {
+ fprintf(stderr, "%s: alignment %zu is not a power of 2\n", __func__, ctx->alignment);
+ gguf_free(ctx);
+ return nullptr;
+ }
+ }
+
+ // read the tensor info
+ for (int64_t i = 0; ok && i < n_tensors; ++i) {
+ struct gguf_tensor_info info;
+
+ // tensor name
+ {
+ std::string name;
+ try {
+ ok = ok && gr.read(name);
+ } catch (std::length_error &) {
+ fprintf(stderr, "%s: encountered length_error while reading tensor name %" PRIi64 "\n", __func__, i);
+ ok = false;
+ } catch (std::bad_alloc &) {
+ fprintf(stderr, "%s: encountered bad_alloc error while reading tensor name %" PRIi64 "\n", __func__, i);
+ ok = false;
+ }
+ if (name.length() >= GGML_MAX_NAME) {
+ fprintf(stderr, "%s: tensor name %" PRIi64 " is too long: %zu >= %d\n", __func__, i, name.length(), GGML_MAX_NAME);
+ ok = false;
+ break;
+ }
+ ggml_set_name(&info.t, name.c_str());
+
+ // make sure there are no duplicate tensor names
+ for (int64_t j = 0; ok && j < i; ++j) {
+ if (strcmp(info.t.name, ctx->info[j].t.name) == 0) {
+ fprintf(stderr, "%s: duplicate tensor name '%s' for tensors %" PRIi64 " and %" PRIi64 "\n", __func__, info.t.name, j, i);
+ ok = false;
+ break;
+ }
+ }
+ }
+ if (!ok) {
+ break;
+ }
+
+ // tensor shape
+ {
+ uint32_t n_dims = -1;
+ ok = ok && gr.read(n_dims);
+ if (n_dims > GGML_MAX_DIMS) {
+ fprintf(stderr, "%s: tensor '%s' has invalid number of dimensions: %" PRIu32 " > %" PRIu32 "\n",
+ __func__, info.t.name, n_dims, GGML_MAX_DIMS);
+ ok = false;
+ break;
+ }
+ for (uint32_t j = 0; ok && j < GGML_MAX_DIMS; ++j) {
+ info.t.ne[j] = 1;
+ if (j < n_dims) {
+ ok = ok && gr.read(info.t.ne[j]);
+ }
+
+ // check that all ne are non-negative
+ if (info.t.ne[j] < 0) {
+ fprintf(stderr, "%s: tensor '%s' dimension %" PRIu32 " has invalid number of elements: %" PRIi64 " < 0\n",
+ __func__, info.t.name, j, info.t.ne[j]);
+ ok = false;
+ break;
+ }
+ }
+
+ // check that the total number of elements is representable
+ if (ok && ((INT64_MAX/info.t.ne[1] <= info.t.ne[0]) ||
+ (INT64_MAX/info.t.ne[2] <= info.t.ne[0]*info.t.ne[1]) ||
+ (INT64_MAX/info.t.ne[3] <= info.t.ne[0]*info.t.ne[1]*info.t.ne[2]))) {
+
+ fprintf(stderr, "%s: total number of elements in tensor '%s' with shape "
+ "(%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") is >= %" PRIi64 "\n",
+ __func__, info.t.name, info.t.ne[0], info.t.ne[1], info.t.ne[2], info.t.ne[3], INT64_MAX);
+ ok = false;
+ break;
+ }
+ }
+ if (!ok) {
+ break;
+ }
+
+ // tensor type
+ {
+ ok = ok && gr.read(info.t.type);
+
+ // check that tensor type is within defined range
+ if (info.t.type < 0 || info.t.type >= GGML_TYPE_COUNT) {
+ fprintf(stderr, "%s: tensor '%s' has invalid ggml type %d (%s)\n",
+ __func__, info.t.name, info.t.type, ggml_type_name(info.t.type));
+ ok = false;
+ break;
+ }
+ const size_t type_size = ggml_type_size(info.t.type);
+ const int64_t blck_size = ggml_blck_size(info.t.type);
+
+ // check that row size is divisible by block size
+ if (blck_size == 0 || info.t.ne[0] % blck_size != 0) {
+ fprintf(stderr, "%s: tensor '%s' of type %d (%s) has %" PRId64 " elements per row, "
+ "not a multiple of block size (%" PRId64 ")\n",
+ __func__, info.t.name, (int) info.t.type, ggml_type_name(info.t.type), info.t.ne[0], blck_size);
+ ok = false;
+ break;
+ }
+
+ // calculate byte offsets given the tensor shape and type
+ info.t.nb[0] = type_size;
+ info.t.nb[1] = info.t.nb[0]*(info.t.ne[0]/blck_size);
+ for (int j = 2; j < GGML_MAX_DIMS; ++j) {
+ info.t.nb[j] = info.t.nb[j - 1]*info.t.ne[j - 1];
+ }
+ }
+ if (!ok) {
+ break;
+ }
+
+ // tensor data offset within buffer
+ ok = ok && gr.read(info.offset);
+
+ ctx->info.push_back(info);
+ }
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to read tensor info\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+ GGML_ASSERT(int64_t(ctx->info.size()) == n_tensors);
+
+ // we require the data section to be aligned, so take into account any padding
+ if (fseek(file, GGML_PAD(ftell(file), ctx->alignment), SEEK_SET) != 0) {
+ fprintf(stderr, "%s: failed to seek to beginning of data section\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ // store the current file offset - this is where the data section starts
+ ctx->offset = ftell(file);
+
+ // compute the total size of the data section, taking into account the alignment
+ {
+ ctx->size = 0;
+ for (size_t i = 0; i < ctx->info.size(); ++i) {
+ const gguf_tensor_info & ti = ctx->info[i];
+ if (ti.offset != ctx->size) {
+ fprintf(stderr, "%s: tensor '%s' has offset %" PRIu64 ", expected %zu\n",
+ __func__, ti.t.name, ti.offset, ctx->size);
+ fprintf(stderr, "%s: failed to read tensor data\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+ ctx->size += GGML_PAD(ggml_nbytes(&ti.t), ctx->alignment);
+ }
+ }
+
+ // load the tensor data only if requested
+ if (params.ctx != nullptr) {
+ // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob
+ // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of
+ // the ggml_tensor structs to the appropriate locations in the binary blob
+
+ // compute the exact size needed for the new ggml_context
+ const size_t mem_size =
+ params.no_alloc ?
+ (n_tensors )*ggml_tensor_overhead() :
+ (n_tensors + 1)*ggml_tensor_overhead() + ctx->size;
+
+ struct ggml_init_params pdata = {
+ /*mem_size =*/ mem_size,
+ /*mem_buffer =*/ nullptr,
+ /*no_alloc =*/ params.no_alloc,
+ };
+
+ *params.ctx = ggml_init(pdata);
+ if (*params.ctx == nullptr) {
+ fprintf(stderr, "%s: failed to initialize ggml context for storing tensors\n", __func__);
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ struct ggml_context * ctx_data = *params.ctx;
+
+ struct ggml_tensor * data = nullptr;
+
+ if (!params.no_alloc) {
+ data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
+
+ ok = ok && data != nullptr;
+
+ // read the binary blob with the tensor data
+ ok = ok && gr.read(data->data, ctx->size);
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to read tensor data binary blob\n", __func__);
+ ggml_free(ctx_data);
+ *params.ctx = nullptr;
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ ctx->data = data->data;
+ }
+
+ ggml_set_no_alloc(ctx_data, true);
+
+ // create the tensors
+ for (size_t i = 0; i < ctx->info.size(); ++i) {
+ const struct gguf_tensor_info & info = ctx->info[i];
+
+ struct ggml_tensor * cur = ggml_new_tensor(ctx_data, info.t.type, GGML_MAX_DIMS, info.t.ne);
+
+ ok = ok && cur != nullptr;
+
+ if (!ok) {
+ break;
+ }
+
+ ggml_set_name(cur, info.t.name);
+
+ // point the data member to the appropriate location in the binary blob using the tensor info
+ if (!params.no_alloc) {
+ cur->data = (char *) data->data + info.offset;
+ }
+ }
+
+ if (!ok) {
+ fprintf(stderr, "%s: failed to create tensors\n", __func__);
+ ggml_free(ctx_data);
+ *params.ctx = nullptr;
+ gguf_free(ctx);
+ return nullptr;
+ }
+
+ ggml_set_no_alloc(ctx_data, params.no_alloc);
+ }
+
+ return ctx;
+}
+
+struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
+ FILE * file = ggml_fopen(fname, "rb");
+
+ if (!file) {
+ fprintf(stderr, "%s: failed to open GGUF file '%s'\n", __func__, fname);
+ return nullptr;
+ }
+
+ struct gguf_context * result = gguf_init_from_file_impl(file, params);
+ fclose(file);
+ return result;
+}
+
+void gguf_free(struct gguf_context * ctx) {
+ if (ctx == nullptr) {
+ return;
+ }
+ delete ctx;
+}
+
+const char * gguf_type_name(enum gguf_type type) {
+ auto it = GGUF_TYPE_NAME.find(type);
+ return it == GGUF_TYPE_NAME.end() ? nullptr : it->second;
+}
+
+uint32_t gguf_get_version(const struct gguf_context * ctx) {
+ return ctx->version;
+}
+
+size_t gguf_get_alignment(const struct gguf_context * ctx) {
+ return ctx->alignment;
+}
+
+size_t gguf_get_data_offset(const struct gguf_context * ctx) {
+ return ctx->offset;
+}
+
+int64_t gguf_get_n_kv(const struct gguf_context * ctx) {
+ return ctx->kv.size();
+}
+
+int64_t gguf_find_key(const struct gguf_context * ctx, const char * key) {
+ // return -1 if key not found
+ int64_t keyfound = -1;
+
+ const int64_t n_kv = gguf_get_n_kv(ctx);
+
+ for (int64_t i = 0; i < n_kv; ++i) {
+ if (strcmp(key, gguf_get_key(ctx, i)) == 0) {
+ keyfound = i;
+ break;
+ }
+ }
+
+ return keyfound;
+}
+
+const char * gguf_get_key(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ return ctx->kv[key_id].get_key().c_str();
+}
+
+enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ return ctx->kv[key_id].is_array ? GGUF_TYPE_ARRAY : ctx->kv[key_id].get_type();
+}
+
+enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].is_array);
+ return ctx->kv[key_id].get_type();
+}
+
+const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+ return ctx->kv[key_id].data.data();
+}
+
+const char * gguf_get_arr_str(const struct gguf_context * ctx, int64_t key_id, size_t i) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_type() == GGUF_TYPE_STRING);
+ return ctx->kv[key_id].data_string[i].c_str();
+}
+
+size_t gguf_get_arr_n(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+
+ if (ctx->kv[key_id].type == GGUF_TYPE_STRING) {
+ return ctx->kv[key_id].data_string.size();
+ }
+
+ const size_t type_size = gguf_type_size(ctx->kv[key_id].type);
+ GGML_ASSERT(ctx->kv[key_id].data.size() % type_size == 0);
+ return ctx->kv[key_id].data.size() / type_size;
+}
+
+uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<uint8_t>();
+}
+
+int8_t gguf_get_val_i8(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<int8_t>();
+}
+
+uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<uint16_t>();
+}
+
+int16_t gguf_get_val_i16(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<int16_t>();
+}
+
+uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<uint32_t>();
+}
+
+int32_t gguf_get_val_i32(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<int32_t>();
+}
+
+float gguf_get_val_f32(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<float>();
+}
+
+uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<uint64_t>();
+}
+
+int64_t gguf_get_val_i64(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<int64_t>();
+}
+
+double gguf_get_val_f64(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<double>();
+}
+
+bool gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<bool>();
+}
+
+const char * gguf_get_val_str(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ return ctx->kv[key_id].get_val<std::string>().c_str();
+}
+
+const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id) {
+ GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+ GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+ GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+ return ctx->kv[key_id].data.data();
+}
+
+int64_t gguf_get_n_tensors(const struct gguf_context * ctx) {
+ return ctx->info.size();
+}
+
+int64_t gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
+ // return -1 if tensor not found
+ int64_t tensor_id = -1;
+
+ const int64_t n_tensors = gguf_get_n_tensors(ctx);
+
+ for (int64_t i = 0; i < n_tensors; ++i) {
+ if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) {
+ tensor_id = i;
+ break;
+ }
+ }
+
+ return tensor_id;
+}
+
+size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id) {
+ GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+ return ctx->info[tensor_id].offset;
+}
+
+const char * gguf_get_tensor_name(const struct gguf_context * ctx, int64_t tensor_id) {
+ GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+ return ctx->info[tensor_id].t.name;
+}
+
+enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int64_t tensor_id) {
+ GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+ return ctx->info[tensor_id].t.type;
+}
+
+size_t gguf_get_tensor_size(const struct gguf_context * ctx, int64_t tensor_id) {
+ GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+ return ggml_nbytes(&ctx->info[tensor_id].t);
+}
+
+int64_t gguf_remove_key(struct gguf_context * ctx, const char * key) {
+ const int64_t key_id = gguf_find_key(ctx, key);
+ if (key_id >= 0) {
+ ctx->kv.erase(ctx->kv.begin() + key_id);
+ }
+ return key_id;
+}
+
+template<typename T>
+static void gguf_check_reserved_keys(const std::string & key, const T val) {
+ if (key == GGUF_KEY_GENERAL_ALIGNMENT) {
+ if constexpr (std::is_same<T, uint32_t>::value) {
+ GGML_ASSERT(val > 0 && (val & (val - 1)) == 0 && GGUF_KEY_GENERAL_ALIGNMENT " must be power of 2");
+ } else {
+ GGML_ABORT(GGUF_KEY_GENERAL_ALIGNMENT " must be type u32");
+ }
+ }
+}
+
+void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) {
+ gguf_check_reserved_keys(key, val);
+ gguf_remove_key(ctx, key);
+ ctx->kv.emplace_back(key, std::string(val));
+}
+
+void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n) {
+ gguf_check_reserved_keys(key, data);
+ gguf_remove_key(ctx, key);
+
+ const size_t nbytes = n*gguf_type_size(type);
+ std::vector<int8_t> tmp(nbytes);
+ if (!tmp.empty()) {
+ memcpy(tmp.data(), data, nbytes);
+ }
+ ctx->kv.emplace_back(key, tmp);
+ ctx->kv.back().cast(type);
+}
+
+void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, size_t n) {
+ gguf_check_reserved_keys(key, data);
+ gguf_remove_key(ctx, key);
+
+ std::vector<std::string> tmp(n);
+ for (size_t i = 0; i < n; ++i) {
+ tmp[i] = data[i];
+ }
+ ctx->kv.emplace_back(key, tmp);
+}
+
+// set or add KV pairs from another context
+void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src) {
+ const int64_t n_kv = gguf_get_n_kv(src);
+ for (int64_t i = 0; i < n_kv; ++i) {
+ const struct gguf_kv & kv = src->kv[i];
+
+ if (!kv.is_array) {
+ switch (kv.get_type()) {
+ case GGUF_TYPE_UINT8: gguf_set_val_u8 (ctx, kv.get_key().c_str(), kv.get_val<uint8_t>()); break;
+ case GGUF_TYPE_INT8: gguf_set_val_i8 (ctx, kv.get_key().c_str(), kv.get_val<int8_t>()); break;
+ case GGUF_TYPE_UINT16: gguf_set_val_u16 (ctx, kv.get_key().c_str(), kv.get_val<uint16_t>()); break;
+ case GGUF_TYPE_INT16: gguf_set_val_i16 (ctx, kv.get_key().c_str(), kv.get_val<int16_t>()); break;
+ case GGUF_TYPE_UINT32: gguf_set_val_u32 (ctx, kv.get_key().c_str(), kv.get_val<uint32_t>()); break;
+ case GGUF_TYPE_INT32: gguf_set_val_i32 (ctx, kv.get_key().c_str(), kv.get_val<int32_t>()); break;
+ case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, kv.get_key().c_str(), kv.get_val<float>()); break;
+ case GGUF_TYPE_UINT64: gguf_set_val_u64 (ctx, kv.get_key().c_str(), kv.get_val<uint64_t>()); break;
+ case GGUF_TYPE_INT64: gguf_set_val_i64 (ctx, kv.get_key().c_str(), kv.get_val<int64_t>()); break;
+ case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, kv.get_key().c_str(), kv.get_val<double>()); break;
+ case GGUF_TYPE_BOOL: gguf_set_val_bool(ctx, kv.get_key().c_str(), kv.get_val<bool>()); break;
+ case GGUF_TYPE_STRING: gguf_set_val_str (ctx, kv.get_key().c_str(), kv.get_val<std::string>().c_str()); break;
+ case GGUF_TYPE_ARRAY:
+ default: GGML_ABORT("invalid type");
+ }
+ continue;
+ }
+
+ const size_t ne = kv.get_ne();
+
+ switch (kv.get_type()) {
+ case GGUF_TYPE_UINT8:
+ case GGUF_TYPE_INT8:
+ case GGUF_TYPE_UINT16:
+ case GGUF_TYPE_INT16:
+ case GGUF_TYPE_UINT32:
+ case GGUF_TYPE_INT32:
+ case GGUF_TYPE_FLOAT32:
+ case GGUF_TYPE_UINT64:
+ case GGUF_TYPE_INT64:
+ case GGUF_TYPE_FLOAT64:
+ case GGUF_TYPE_BOOL: {
+ gguf_set_arr_data(ctx, kv.get_key().c_str(), kv.get_type(), kv.data.data(), ne);
+ } break;
+ case GGUF_TYPE_STRING: {
+ std::vector<const char *> tmp(ne);
+ for (size_t j = 0; j < ne; ++j) {
+ tmp[j] = kv.data_string[j].c_str();
+ }
+ gguf_set_arr_str(ctx, kv.get_key().c_str(), tmp.data(), ne);
+ } break;
+ case GGUF_TYPE_ARRAY:
+ default: GGML_ABORT("invalid type");
+ }
+ }
+}
+
+void gguf_add_tensor(
+ struct gguf_context * ctx,
+ const struct ggml_tensor * tensor) {
+ GGML_ASSERT(tensor);
+ if (gguf_find_tensor(ctx, tensor->name) != -1) {
+ GGML_ABORT("duplicate tensor name: %s", tensor->name);
+ }
+
+ struct gguf_tensor_info ti;
+ ti.t = *tensor;
+ ti.offset = ctx->info.empty() ? 0 :
+ ctx->info.back().offset + GGML_PAD(ggml_nbytes(&ctx->info.back().t), ctx->alignment);
+ ctx->info.push_back(ti);
+}
+
+void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
+ const int64_t tensor_id = gguf_find_tensor(ctx, name);
+ if (tensor_id < 0) {
+ GGML_ABORT("tensor not found: %s", name);
+ }
+ struct ggml_tensor * tensor = &ctx->info[tensor_id].t;
+ const size_t type_size = ggml_type_size(type);
+ const int64_t blck_size = ggml_blck_size(type);
+
+ tensor->type = type;
+ GGML_ASSERT(tensor->ne[0] % blck_size == 0 && "tensor row size not divisible by block size of new type");
+
+ tensor->nb[0] = type_size;
+ tensor->nb[1] = tensor->nb[0]*(tensor->ne[0]/blck_size);
+ for (int i = 2; i < GGML_MAX_DIMS; i++) {
+ tensor->nb[i] = tensor->nb[i - 1]*tensor->ne[i - 1];
+ }
+
+ // update offsets
+ const int64_t n_tensors = gguf_get_n_tensors(ctx);
+ for (int64_t i = tensor_id + 1; i < n_tensors; ++i) {
+ ctx->info[i].offset = ctx->info[i - 1].offset + GGML_PAD(ggml_nbytes(&ctx->info[i - 1].t), ctx->alignment);
+ }
+}
+
+void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data) {
+ const int64_t tensor_id = gguf_find_tensor(ctx, name);
+ if (tensor_id < 0) {
+ GGML_ABORT("tensor not found: %s", name);
+ }
+
+ ctx->info[tensor_id].t.data = (void *)(uintptr_t)data; // double cast suppresses warning about casting away const
+}
+
+struct gguf_writer {
+ std::vector<int8_t> & buf;
+
+ gguf_writer(std::vector<int8_t> & buf) : buf(buf) {}
+
+ template <typename T>
+ void write(const T & val) const {
+ for (size_t i = 0; i < sizeof(val); ++i) {
+ buf.push_back(reinterpret_cast<const int8_t *>(&val)[i]);
+ }
+ }
+
+ void write(const std::vector<int8_t> & val) const {
+ buf.insert(buf.end(), val.begin(), val.end());
+ }
+
+ void write(const bool & val) const {
+ const int8_t val8 = val ? 1 : 0;
+ write(val8);
+ }
+
+ void write(const std::string & val) const {
+ {
+ const uint64_t n = val.length();
+ write(n);
+ }
+ for (size_t i = 0; i < val.length(); ++i) {
+ buf.push_back(reinterpret_cast<const int8_t *>(val.data())[i]);
+ }
+ }
+
+ void write(const char * val) const {
+ write(std::string(val));
+ }
+
+ void write(const enum ggml_type & val) const {
+ write(int32_t(val));
+ }
+
+ void write(const enum gguf_type & val) const {
+ write(int32_t(val));
+ }
+
+ void write(const struct gguf_kv & kv) const {
+ const uint64_t ne = kv.get_ne();
+
+ write(kv.get_key());
+
+ if (kv.is_array) {
+ write(GGUF_TYPE_ARRAY);
+ write(kv.get_type());
+ write(ne);
+ } else {
+ write(kv.get_type());
+ }
+
+ switch (kv.get_type()) {
+ case GGUF_TYPE_UINT8:
+ case GGUF_TYPE_INT8:
+ case GGUF_TYPE_UINT16:
+ case GGUF_TYPE_INT16:
+ case GGUF_TYPE_UINT32:
+ case GGUF_TYPE_INT32:
+ case GGUF_TYPE_FLOAT32:
+ case GGUF_TYPE_UINT64:
+ case GGUF_TYPE_INT64:
+ case GGUF_TYPE_FLOAT64: {
+ write(kv.data);
+ } break;
+ case GGUF_TYPE_BOOL: {
+ for (size_t i = 0; i < ne; ++i) {
+ write(kv.get_val<bool>(i));
+ }
+ } break;
+ case GGUF_TYPE_STRING: {
+ for (size_t i = 0; i < ne; ++i) {
+ write(kv.get_val<std::string>(i));
+ }
+ } break;
+ case GGUF_TYPE_ARRAY:
+ default: GGML_ABORT("invalid type");
+ }
+ }
+
+ void write_tensor_meta(const struct gguf_tensor_info & info) const {
+ write(info.t.name);
+
+ const uint32_t n_dims = ggml_n_dims(&info.t);
+ write(n_dims);
+
+ for (uint32_t j = 0; j < n_dims; ++j) {
+ write(info.t.ne[j]);
+ }
+ write(info.t.type);
+ write(info.offset);
+ }
+
+ void pad(const size_t alignment) const {
+ while (buf.size() % alignment != 0) {
+ const int8_t zero = 0;
+ write(zero);
+ }
+ }
+
+ void write_tensor_data(const struct gguf_tensor_info & info, const size_t offset_data, const size_t alignment) const {
+ GGML_ASSERT(buf.size() - offset_data == info.offset);
+
+ GGML_ASSERT(ggml_is_contiguous(&info.t));
+ const size_t offset = buf.size();
+ const size_t nbytes = ggml_nbytes(&info.t);
+
+ buf.resize(offset + nbytes);
+ if (info.t.buffer) {
+ ggml_backend_tensor_get(&info.t, buf.data() + offset, 0, nbytes);
+ } else {
+ GGML_ASSERT(info.t.data);
+ memcpy(buf.data() + offset, info.t.data, nbytes);
+ }
+
+ pad(alignment);
+ }
+};
+
+void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta) {
+ const struct gguf_writer gw(buf);
+
+ const int64_t n_kv = gguf_get_n_kv(ctx);
+ const int64_t n_tensors = gguf_get_n_tensors(ctx);
+
+ // write header
+ gw.write(GGUF_MAGIC[0]);
+ gw.write(GGUF_MAGIC[1]);
+ gw.write(GGUF_MAGIC[2]);
+ gw.write(GGUF_MAGIC[3]);
+ gw.write(ctx->version);
+ gw.write(n_tensors);
+ gw.write(n_kv);
+
+ // write key-value pairs
+ for (int64_t i = 0; i < n_kv; ++i) {
+ gw.write(ctx->kv[i]);
+ }
+
+ // write tensor info
+ for (int64_t i = 0; i < n_tensors; ++i) {
+ gw.write_tensor_meta(ctx->info[i]);
+ }
+
+ // we require the data section to be aligned
+ gw.pad(ctx->alignment);
+
+ if (only_meta) {
+ return;
+ }
+
+ const size_t offset_data = gw.buf.size();
+
+ // write tensor data
+ for (int64_t i = 0; i < n_tensors; ++i) {
+ gw.write_tensor_data(ctx->info[i], offset_data, ctx->alignment);
+ }
+}
+
+bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
+ FILE * file = ggml_fopen(fname, "wb");
+
+ if (!file) {
+ fprintf(stderr, "%s: failed to open file '%s' for writing GGUF data\n", __func__, fname);
+ return false;
+ }
+
+ std::vector<int8_t> buf;
+ gguf_write_to_buf(ctx, buf, only_meta);
+ const bool ok = fwrite(buf.data(), 1, buf.size(), file) == buf.size();
+ fclose(file);
+ return ok;
+}
+
+size_t gguf_get_meta_size(const struct gguf_context * ctx) {
+ // only return size
+ std::vector<int8_t> buf;
+ gguf_write_to_buf(ctx, buf, /*only_meta =*/ true);
+ return buf.size();
+}
+
+void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
+ std::vector<int8_t> buf;
+ gguf_write_to_buf(ctx, buf, /*only_meta =*/ true);
+ memcpy(data, buf.data(), buf.size());
+}
overview / pkg / ggml / sources / whisper.cpp / commitdiff