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1 files changed, 1355 insertions, 0 deletions

diff --git a/llama.cpp/tests/test-gguf.cpp b/llama.cpp/tests/test-gguf.cpp
new file mode 100644
index 0000000..84b7f3b
--- /dev/null
+++ b/llama.cpp/tests/test-gguf.cpp
@@ -0,0 +1,1355 @@
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "../ggml/src/ggml-impl.h"
+#include "gguf.h"
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <random>
+#include <string>
+#include <vector>
+
+constexpr int offset_has_kv      = 1000;
+constexpr int offset_has_tensors = 2000;
+constexpr int offset_has_data    = 3000;
+
+enum handcrafted_file_type {
+    HANDCRAFTED_HEADER_BAD_MAGIC           =  10,
+    HANDCRAFTED_HEADER_BAD_VERSION_0       =  15,
+    HANDCRAFTED_HEADER_BAD_VERSION_1       =  20,
+    HANDCRAFTED_HEADER_BAD_VERSION_FUTURE  =  30,
+    HANDCRAFTED_HEADER_BAD_N_TENSORS       =  40,
+    HANDCRAFTED_HEADER_BAD_N_KV            =  50,
+    HANDCRAFTED_HEADER_EMPTY               = 800,
+
+    HANDCRAFTED_KV_BAD_KEY_SIZE            =  10 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_TYPE                =  20 + offset_has_kv,
+    // HANDCRAFTED_KV_BAD_VALUE_SIZE          =  30 + offset_has_kv, // removed because it can result in allocations > 1 TB (default sanitizer limit)
+    HANDCRAFTED_KV_DUPLICATE_KEY           =  40 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_ALIGN               =  50 + offset_has_kv,
+    HANDCRAFTED_KV_SUCCESS                 = 800 + offset_has_kv,
+
+    HANDCRAFTED_TENSORS_BAD_NAME_SIZE      =  10 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_N_DIMS         =  20 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_SHAPE          =  30 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_NE_TOO_BIG         =  40 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_NBYTES_TOO_BIG     =  45 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_TYPE           =  50 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_OFFSET         =  60 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_DUPLICATE_NAME     =  70 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_ALIGN          =  75 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN =  80 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_SUCCESS            = 800 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_CUSTOM_ALIGN       = 810 + offset_has_tensors,
+
+    HANDCRAFTED_DATA_NOT_ENOUGH_DATA       =  10 + offset_has_data,
+    HANDCRAFTED_DATA_BAD_ALIGN             =  15 + offset_has_data,
+    HANDCRAFTED_DATA_INCONSISTENT_ALIGN    =  20 + offset_has_data,
+    HANDCRAFTED_DATA_SUCCESS               = 800 + offset_has_data,
+    HANDCRAFTED_DATA_CUSTOM_ALIGN          = 810 + offset_has_data,
+};
+
+static std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
+    switch (hft) {
+        case HANDCRAFTED_HEADER_BAD_MAGIC:           return "HEADER_BAD_MAGIC";
+        case HANDCRAFTED_HEADER_BAD_VERSION_0:       return "HEADER_BAD_VERSION_0";
+        case HANDCRAFTED_HEADER_BAD_VERSION_1:       return "HEADER_BAD_VERSION_1";
+        case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE:  return "HEADER_BAD_VERSION_FUTURE";
+        case HANDCRAFTED_HEADER_BAD_N_KV:            return "HEADER_BAD_N_KV";
+        case HANDCRAFTED_HEADER_BAD_N_TENSORS:       return "HEADER_BAD_N_TENSORS";
+        case HANDCRAFTED_HEADER_EMPTY:               return "HEADER_EMPTY";
+
+        case HANDCRAFTED_KV_BAD_KEY_SIZE:            return "KV_BAD_KEY_SIZE";
+        case HANDCRAFTED_KV_BAD_TYPE:                return "KV_BAD_TYPE";
+        case HANDCRAFTED_KV_DUPLICATE_KEY:           return "KV_DUPLICATE_KEY";
+        case HANDCRAFTED_KV_BAD_ALIGN:               return "KV_BAD_ALIGN";
+        case HANDCRAFTED_KV_SUCCESS:                 return "KV_RANDOM_KV";
+
+        case HANDCRAFTED_TENSORS_BAD_NAME_SIZE:      return "TENSORS_BAD_NAME_SIZE";
+        case HANDCRAFTED_TENSORS_BAD_N_DIMS:         return "TENSORS_BAD_N_DIMS";
+        case HANDCRAFTED_TENSORS_BAD_SHAPE:          return "TENSORS_BAD_SHAPE";
+        case HANDCRAFTED_TENSORS_NE_TOO_BIG:         return "TENSORS_NE_TOO_BIG";
+        case HANDCRAFTED_TENSORS_NBYTES_TOO_BIG:     return "TENSORS_NBYTES_TOO_BIG";
+        case HANDCRAFTED_TENSORS_BAD_TYPE:           return "TENSORS_BAD_TYPE";
+        case HANDCRAFTED_TENSORS_BAD_OFFSET:         return "TENSORS_BAD_OFFSET";
+        case HANDCRAFTED_TENSORS_DUPLICATE_NAME:     return "TENSORS_DUPLICATE_NAME";
+        case HANDCRAFTED_TENSORS_BAD_ALIGN:          return "TENSORS_BAD_ALIGN";
+        case HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN: return "TENSORS_INCONSISTENT_ALIGN";
+        case HANDCRAFTED_TENSORS_SUCCESS:            return "TENSORS_SUCCESS";
+        case HANDCRAFTED_TENSORS_CUSTOM_ALIGN:       return "TENSORS_CUSTOM_ALIGN";
+
+        case HANDCRAFTED_DATA_NOT_ENOUGH_DATA:       return "DATA_NOT_ENOUGH_DATA";
+        case HANDCRAFTED_DATA_BAD_ALIGN:             return "DATA_BAD_ALIGN";
+        case HANDCRAFTED_DATA_INCONSISTENT_ALIGN:    return "DATA_INCONSISTENT_ALIGN";
+        case HANDCRAFTED_DATA_SUCCESS:               return "DATA_SUCCESS";
+        case HANDCRAFTED_DATA_CUSTOM_ALIGN:          return "DATA_CUSTOM_ALIGN";
+    }
+    GGML_ABORT("fatal error");
+}
+
+static bool expect_context_not_null(const enum handcrafted_file_type hft) {
+    if (hft < offset_has_kv) {
+        return hft >= HANDCRAFTED_HEADER_EMPTY;
+    }
+    if (hft < offset_has_tensors) {
+        return hft >= HANDCRAFTED_KV_SUCCESS;
+    }
+    if (hft < offset_has_data) {
+        return hft >= HANDCRAFTED_TENSORS_SUCCESS;
+    }
+    return hft >= HANDCRAFTED_DATA_SUCCESS;
+}
+
+typedef std::pair<enum ggml_type, std::array<int64_t, GGML_MAX_DIMS>> tensor_config_t;
+
+static std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
+    std::vector<tensor_config_t> tensor_configs;
+    tensor_configs.reserve(100);
+
+    for (int i = 0; i < 100; ++i) {
+        const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT);
+        if (ggml_type_size(type) == 0) {
+            continue;
+        }
+
+        std::array<int64_t, GGML_MAX_DIMS> shape = {1, 1, 1, 1};
+        shape[0] = (1 + rng() % 10) * ggml_blck_size(type);
+        const int n_dims = 1 + rng() % GGML_MAX_DIMS;
+        for (int i = 1; i < n_dims; ++i) {
+            shape[i] = 1 + rng() % 10;
+        }
+
+        tensor_configs.push_back(std::make_pair(type, shape));
+    }
+
+    return tensor_configs;
+}
+
+static std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    kv_types.reserve(100);
+
+    for (int i = 0; i < 100; ++i) {
+        const gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT);
+            if (type_arr == GGUF_TYPE_ARRAY) {
+                continue;
+            }
+            kv_types.push_back(std::make_pair(type, type_arr));
+            continue;
+        }
+
+        kv_types.push_back(std::make_pair(type, gguf_type(-1)));
+    }
+    std::shuffle(kv_types.begin(), kv_types.end(), rng);
+
+    return kv_types;
+}
+
+template <typename T>
+static void helper_write(FILE * file, const T & val) {
+    GGML_ASSERT(fwrite(&val, 1, sizeof(val), file) == sizeof(val));
+}
+
+static void helper_write(FILE * file, const void * data, const size_t nbytes) {
+    GGML_ASSERT(fwrite(data, 1, nbytes, file) == nbytes);
+}
+
+static FILE * get_handcrafted_file(const unsigned int seed, const enum handcrafted_file_type hft, const int extra_bytes = 0) {
+    FILE * file = tmpfile();
+
+    if (!file) {
+        return file;
+    }
+
+    std::mt19937 rng(seed);
+    uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
+
+    if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) {
+        const char bad_magic[4] = {'F', 'U', 'G', 'G'};
+        helper_write(file, bad_magic, sizeof(bad_magic));
+    } else {
+        helper_write(file, GGUF_MAGIC, 4);
+    }
+
+    if (hft == HANDCRAFTED_HEADER_BAD_VERSION_0) {
+        const uint32_t version = 0;
+        helper_write(file, version);
+    } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) {
+        const uint32_t version = 1;
+        helper_write(file, version);
+    } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) {
+        const uint32_t version = GGUF_VERSION + 1;
+        helper_write(file, version);
+    } else {
+        const uint32_t version = GGUF_VERSION;
+        helper_write(file, version);
+    }
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (hft >= offset_has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+
+    if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) {
+        const uint64_t n_tensors = -1;
+        helper_write(file, n_tensors);
+    } else {
+        const uint64_t n_tensors = tensor_configs.size();
+        helper_write(file, n_tensors);
+    }
+
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    if (hft >= offset_has_kv) {
+        kv_types = get_kv_types(rng);
+    }
+    {
+        uint64_t n_kv = kv_types.size();
+        if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
+            hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+            hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+
+            n_kv += 1;
+        } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
+            n_kv = -1;
+        }
+        helper_write(file, n_kv);
+    }
+
+    if (hft < offset_has_kv) {
+        while (ftell(file) % alignment != 0) {
+            const char pad = 0;
+            helper_write(file, pad);
+        }
+
+        for (int i = 0; i < extra_bytes; ++i) {
+            const char tmp = 0;
+            helper_write(file, tmp);
+        }
+        rewind(file);
+        return file;
+    }
+
+    for (int i = 0; i < int(kv_types.size()); ++i) {
+        const enum gguf_type type     = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].first);
+        const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].second);
+
+        const std::string key = "my_key_" + std::to_string((hft == HANDCRAFTED_KV_DUPLICATE_KEY ? i/2 : i));
+
+        if (hft == HANDCRAFTED_KV_BAD_KEY_SIZE) {
+            const uint64_t n = -1;
+            helper_write(file, n);
+        } else {
+            const uint64_t n = key.length();
+            helper_write(file, n);
+        }
+        helper_write(file, key.data(), key.length());
+
+        {
+            const int32_t type32 = int32_t(type);
+            helper_write(file, type32);
+        }
+
+        uint32_t data[16];
+        for (int j = 0; j < 16; ++j) {
+            data[j] = rng();
+            if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) {
+                data[j] |= 0x01010101; // avoid random null-termination of string
+            }
+        }
+
+        if (type == GGUF_TYPE_STRING) {
+            const uint64_t n = rng() % sizeof(data);
+            helper_write(file, n);
+            helper_write(file, data, n);
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            {
+                const int32_t type32 = int32_t(type_arr);
+                helper_write(file, type32);
+            }
+            if (type_arr == GGUF_TYPE_STRING) {
+                const uint64_t nstr = rng() % (16 + 1);
+                helper_write(file, nstr);
+                for (uint64_t istr = 0; istr < nstr; ++istr) {
+                    const uint64_t n = rng() % (sizeof(uint32_t) + 1);
+                    helper_write(file, n);
+                    helper_write(file, &data[istr], n);
+                }
+                continue;
+            }
+            const size_t type_size = gguf_type_size(type_arr);
+            const uint64_t n = (rng() % sizeof(data)) / type_size;
+            helper_write(file, n);
+            helper_write(file, &data, n*type_size);
+            continue;
+        }
+
+        helper_write(file, data, hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type));
+    }
+
+    if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
+        hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+        hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+
+        const uint64_t n = strlen(GGUF_KEY_GENERAL_ALIGNMENT);
+        helper_write(file, n);
+        helper_write(file, GGUF_KEY_GENERAL_ALIGNMENT, n);
+
+        const int32_t type = gguf_type(GGUF_TYPE_UINT32);
+        helper_write(file, type);
+
+        alignment = expect_context_not_null(hft) ? 1 : 13;
+        helper_write(file, alignment);
+    }
+
+    if (hft < offset_has_tensors) {
+        while (ftell(file) % alignment != 0) {
+            const char pad = 0;
+            helper_write(file, pad);
+        }
+
+        for (int i = 0; i < extra_bytes; ++i) {
+            const char tmp = 0;
+            helper_write(file, tmp);
+        }
+        rewind(file);
+        return file;
+    }
+
+    if (hft == HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN || hft == HANDCRAFTED_DATA_INCONSISTENT_ALIGN) {
+        alignment = 1;
+    }
+
+    uint64_t offset = 0;
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG ? GGML_TYPE_I64 : tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        std::string name = "my_tensor";
+        if (hft != HANDCRAFTED_TENSORS_DUPLICATE_NAME) {
+            name += "_" + std::to_string(i);
+        }
+        if (hft == HANDCRAFTED_TENSORS_BAD_NAME_SIZE) {
+            name += "_with_a_very_long_name_which_is_longer_than_what_is_allowed_for_ggml_tensors";
+            GGML_ASSERT(name.length() >= GGML_MAX_NAME);
+        }
+        {
+            const uint64_t n = name.length();
+            helper_write(file, n);
+        }
+        helper_write(file, name.data(), name.length());
+
+        uint32_t n_dims = (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG || hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG) ? 2 : 1;
+        for (int i = GGML_MAX_DIMS-1; i >= 1; --i) {
+            if (shape[i] != 1) {
+                n_dims = i + 1;
+                break;
+            }
+        }
+        if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) {
+            const uint32_t n_dims_bad = GGML_MAX_DIMS + 1;
+            helper_write(file, n_dims_bad);
+        } else {
+            helper_write(file, n_dims);
+        }
+
+        if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) {
+            const int64_t bad_dim = -1;
+            for (uint32_t j = 0; j < n_dims; ++j) {
+                helper_write(file, bad_dim);
+            }
+        } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
+            const int64_t big_dim = 4*int64_t(INT32_MAX);
+            for (uint32_t j = 0; j < n_dims; ++j) {
+                helper_write(file, big_dim);
+            }
+        } else if (hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG){
+            const size_t  big_ne  = SIZE_MAX/ggml_type_size(type);
+            const int64_t big_dim = GGML_PAD(int64_t(1.01f*std::pow(big_ne, 1.0f/n_dims)) + 1, ggml_blck_size(type));
+            for (uint32_t j = 0; j < n_dims; ++j) {
+                helper_write(file, big_dim);
+            }
+        } else {
+            helper_write(file, shape.data(), n_dims*sizeof(int64_t));
+        }
+
+        {
+            const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? GGML_TYPE_COUNT : int32_t(type);
+            helper_write(file, type32);
+        }
+
+        if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) {
+            const uint64_t bad_offset = -1;
+            helper_write(file, bad_offset);
+        } else {
+            helper_write(file, offset);
+        }
+
+        int64_t ne = shape[0];
+        for (uint32_t i = 1; i < n_dims; ++i) {
+            ne *= shape[i];
+        }
+        offset += GGML_PAD(ggml_row_size(type, ne), alignment);
+    }
+
+    while (ftell(file) % alignment != 0) {
+        const char pad = 0;
+        helper_write(file, pad);
+    }
+
+    if (hft >= offset_has_data) {
+        rng.seed(seed + 1);
+        uint64_t nbytes = offset;
+        if (hft == HANDCRAFTED_DATA_NOT_ENOUGH_DATA) {
+            nbytes -= 1;
+        }
+        for (uint64_t i = 0; i < nbytes; ++i) {
+            const uint8_t random_byte = i % 256;
+            helper_write(file, random_byte);
+        }
+    }
+
+    for (int i = 0; i < extra_bytes; ++i) {
+        const char tmp = 0;
+        helper_write(file, tmp);
+    }
+    rewind(file);
+    return file;
+}
+
+static bool handcrafted_check_header(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_kv, const bool has_tensors, const bool alignment_defined) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    if (has_kv) {
+        kv_types = get_kv_types(rng);
+    }
+
+    bool ok = true;
+
+    if (gguf_get_version(gguf_ctx) != GGUF_VERSION) {
+        ok = false;
+    }
+    if (gguf_get_n_tensors(gguf_ctx) != int(tensor_configs.size())) {
+        ok = false;
+    }
+    if (gguf_get_n_kv(gguf_ctx) != int(alignment_defined ? kv_types.size() + 1 : kv_types.size())) {
+        ok = false;
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_kv(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_tensors, const bool alignment_defined) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types = get_kv_types(rng);
+
+    bool ok = true;
+
+    for (int i = 0; i < int(kv_types.size()); ++i) {
+        const enum gguf_type type     = gguf_type(kv_types[i].first);
+        const enum gguf_type type_arr = gguf_type(kv_types[i].second);
+
+        const std::string key = "my_key_" + std::to_string(i);
+
+        uint32_t data[16];
+        for (int j = 0; j < 16; ++j) {
+            data[j] = rng();
+            if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) {
+                data[j] |= 0x01010101; // avoid random null-termination of string
+            }
+        }
+
+        const char * data8 = reinterpret_cast<const char *>(data);
+        const int id = gguf_find_key(gguf_ctx, key.c_str());
+
+        if (type == GGUF_TYPE_STRING) {
+            const char * str = gguf_get_val_str(gguf_ctx, id);
+            const uint64_t n = strlen(str);
+            const uint64_t n_expected = rng() % sizeof(data);
+            if (n != n_expected) {
+                ok = false;
+                continue;
+            }
+            if (!std::equal(str, str + n, data8)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const size_t type_size = gguf_type_size(type_arr);
+            const uint64_t arr_n = gguf_get_arr_n(gguf_ctx, id);
+
+            if (type_arr == GGUF_TYPE_STRING) {
+                const uint64_t nstr_expected = rng() % (16 + 1);
+                if (arr_n != nstr_expected) {
+                    ok = false;
+                    continue;
+                }
+                for (uint64_t istr = 0; istr < nstr_expected; ++istr) {
+                    const char * str = gguf_get_arr_str(gguf_ctx, id, istr);
+                    const uint64_t n = strlen(str);
+                    const uint64_t n_expected = rng() % (sizeof(uint32_t) + 1);
+
+                    if (n != n_expected) {
+                        ok = false;
+                        continue;
+                    }
+                    const char * str_expected = reinterpret_cast<const char *>(&data[istr]);
+                    if (strncmp(str, str_expected, n) != 0) {
+                        ok = false;
+                        continue;
+                    }
+                }
+                continue;
+            }
+
+            const uint64_t arr_n_expected = (rng() % sizeof(data)) / type_size;
+            if (arr_n != arr_n_expected) {
+                ok = false;
+                continue;
+            }
+
+            const char * data_gguf = reinterpret_cast<const char *>(gguf_get_arr_data(gguf_ctx, id));
+
+            if (type_arr == GGUF_TYPE_BOOL) {
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    if (bool(data8[arr_i]) != bool(data_gguf[arr_i])) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
+            if (!std::equal(data8, data8 + arr_n*type_size, data_gguf)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        const char * data_gguf = reinterpret_cast<const char *>(gguf_get_val_data(gguf_ctx, id));
+
+        if (type == GGUF_TYPE_BOOL) {
+            if (bool(*data8) != bool(*data_gguf)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        if (!std::equal(data8, data8 + gguf_type_size(type), data_gguf)) {
+            ok = false;
+        }
+    }
+
+    const uint32_t expected_alignment = alignment_defined ? 1 : GGUF_DEFAULT_ALIGNMENT;
+    if (gguf_get_alignment(gguf_ctx) != expected_alignment) {
+        ok = false;
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_tensors(const gguf_context * gguf_ctx, const unsigned int seed) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs = get_tensor_configs(rng);
+
+    // Call get_kv_types to get the same RNG state:
+    get_kv_types(rng);
+
+    bool ok = true;
+
+    const int id_alignment = gguf_find_key(gguf_ctx, GGUF_KEY_GENERAL_ALIGNMENT);
+    const uint32_t alignment = id_alignment >= 0 ? gguf_get_val_u32(gguf_ctx, id_alignment) : GGUF_DEFAULT_ALIGNMENT;
+
+    uint64_t expected_offset = 0;
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const int id = gguf_find_tensor(gguf_ctx, name.c_str());
+
+        if (id >= 0) {
+            if (std::string(gguf_get_tensor_name(gguf_ctx, id)) != name) {
+                ok = false;
+            }
+
+            if (gguf_get_tensor_type(gguf_ctx, id) != type) {
+                ok = false;
+            }
+        } else {
+            ok = false;
+            continue;
+        }
+
+        const size_t offset = gguf_get_tensor_offset(gguf_ctx, id);
+
+        if (offset != expected_offset) {
+            ok = false;
+        }
+
+        int64_t ne = shape[0];
+        for (size_t j = 1; j < GGML_MAX_DIMS; ++j) {
+            ne *= shape[j];
+        }
+        expected_offset += GGML_PAD(ggml_row_size(type, ne), alignment);
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const unsigned int seed, FILE * file) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs = get_tensor_configs(rng);
+
+    bool ok = true;
+
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        int64_t ne = shape[0];
+        for (size_t j = 1; j < GGML_MAX_DIMS; ++j) {
+            ne *= shape[j];
+        }
+        const size_t size = ggml_row_size(type, ne);
+
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const size_t offset = gguf_get_tensor_offset(gguf_ctx, gguf_find_tensor(gguf_ctx, name.c_str()));
+
+        std::vector<uint8_t> data(size);
+        GGML_ASSERT(fseek(file, gguf_get_data_offset(gguf_ctx) + offset, SEEK_SET) == 0);
+        GGML_ASSERT(fread(data.data(), 1, data.size(), file) == data.size());
+
+        for (size_t j = 0; j < size; ++j) {
+            const uint8_t expected_byte = (j + offset) % 256;
+            if (data[j] != expected_byte) {
+                ok = false;
+            }
+        }
+    }
+
+    return ok;
+}
+
+static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
+    int npass = 0;
+    int ntest = 0;
+
+    const std::vector<handcrafted_file_type> hfts = {
+        HANDCRAFTED_HEADER_BAD_MAGIC,
+        HANDCRAFTED_HEADER_BAD_VERSION_0,
+        HANDCRAFTED_HEADER_BAD_VERSION_1,
+        HANDCRAFTED_HEADER_BAD_VERSION_FUTURE,
+        HANDCRAFTED_HEADER_BAD_N_KV,
+        HANDCRAFTED_HEADER_BAD_N_TENSORS,
+        HANDCRAFTED_HEADER_EMPTY,
+
+        HANDCRAFTED_KV_BAD_KEY_SIZE,
+        HANDCRAFTED_KV_BAD_TYPE,
+        HANDCRAFTED_KV_DUPLICATE_KEY,
+        HANDCRAFTED_KV_BAD_ALIGN,
+        HANDCRAFTED_KV_SUCCESS,
+
+        HANDCRAFTED_TENSORS_BAD_NAME_SIZE,
+        HANDCRAFTED_TENSORS_BAD_N_DIMS,
+        HANDCRAFTED_TENSORS_BAD_SHAPE,
+        HANDCRAFTED_TENSORS_NE_TOO_BIG,
+        HANDCRAFTED_TENSORS_NBYTES_TOO_BIG,
+        HANDCRAFTED_TENSORS_BAD_TYPE,
+        HANDCRAFTED_TENSORS_BAD_OFFSET,
+        HANDCRAFTED_TENSORS_DUPLICATE_NAME,
+        HANDCRAFTED_TENSORS_BAD_ALIGN,
+        HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN,
+        HANDCRAFTED_TENSORS_SUCCESS,
+        HANDCRAFTED_TENSORS_CUSTOM_ALIGN,
+
+        HANDCRAFTED_DATA_NOT_ENOUGH_DATA,
+        HANDCRAFTED_DATA_BAD_ALIGN,
+        HANDCRAFTED_DATA_INCONSISTENT_ALIGN,
+        HANDCRAFTED_DATA_SUCCESS,
+        HANDCRAFTED_DATA_CUSTOM_ALIGN,
+    };
+
+    for (enum handcrafted_file_type hft : hfts) {
+        printf("%s: handcrafted_file_type=%s\n", __func__, handcrafted_file_type_name(hft).c_str());
+        FILE * file = get_handcrafted_file(seed, hft);
+
+#ifdef _WIN32
+        if (!file) {
+            printf("failed to create tmpfile(), needs elevated privileges on Windows");
+            printf("skipping tests");
+            continue;
+        }
+#else
+        GGML_ASSERT(file);
+#endif // _WIN32
+
+        struct ggml_context * ctx = nullptr;
+        struct gguf_init_params gguf_params = {
+            /*no_alloc =*/ false,
+            /*ctx      =*/ hft >= offset_has_data ? &ctx : nullptr,
+        };
+
+        struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params);
+
+        if (expect_context_not_null(hft)) {
+            printf("%s:   - context_not_null: ", __func__);
+        } else {
+            printf("%s:   - context_null: ", __func__);
+        }
+        if (bool(gguf_ctx) == expect_context_not_null(hft)) {
+            printf("\033[1;32mOK\033[0m\n");
+            npass++;
+        } else {
+            printf("\033[1;31mFAIL\033[0m\n");
+        }
+        ntest++;
+
+        if (hft >= offset_has_data && !expect_context_not_null(hft)) {
+            printf("%s:   - no_dangling_ggml_context_pointer: ", __func__);
+            if (ctx) {
+                printf("\033[1;31mFAIL\033[0m\n");
+            } else {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            }
+            ntest++;
+        }
+
+        const bool alignment_defined = hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN;
+
+        if (expect_context_not_null(hft)) {
+            printf("%s:   - check_header: ", __func__);
+            if (handcrafted_check_header(gguf_ctx, seed, hft >= offset_has_kv, hft >= offset_has_tensors, alignment_defined)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_kv) {
+            printf("%s:   - check_kv: ", __func__);
+            if (handcrafted_check_kv(gguf_ctx, seed, hft >= offset_has_tensors, alignment_defined)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_tensors) {
+            printf("%s:   - check_tensors: ", __func__);
+            if (handcrafted_check_tensors(gguf_ctx, seed)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_data) {
+            printf("%s:   - check_tensor_data: ", __func__);
+            if (handcrafted_check_tensor_data(gguf_ctx, seed, file)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        fclose(file);
+        if (gguf_ctx) {
+            ggml_free(ctx);
+            gguf_free(gguf_ctx);
+        }
+        printf("\n");
+    }
+
+
+    return std::make_pair(npass, ntest);
+}
+
+struct random_gguf_context_result {
+    struct gguf_context * gguf_ctx;
+    struct ggml_context * ctx;
+    ggml_backend_buffer_t buffer;
+};
+
+static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t backend, const unsigned int seed) {
+    std::mt19937 rng(seed);
+
+    struct gguf_context * gguf_ctx = gguf_init_empty();
+
+    for (int i = 0; i < 256; ++i) {
+        const std::string key = "my_key_" + std::to_string(rng() % 1024);
+        const enum gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
+
+        switch (type) {
+            case GGUF_TYPE_UINT8:   gguf_set_val_u8  (gguf_ctx, key.c_str(), rng() % (1 <<  7));             break;
+            case GGUF_TYPE_INT8:    gguf_set_val_i8  (gguf_ctx, key.c_str(), rng() % (1 <<  7) - (1 <<  6)); break;
+            case GGUF_TYPE_UINT16:  gguf_set_val_u16 (gguf_ctx, key.c_str(), rng() % (1 << 15));             break;
+            case GGUF_TYPE_INT16:   gguf_set_val_i16 (gguf_ctx, key.c_str(), rng() % (1 << 15) - (1 << 14)); break;
+            case GGUF_TYPE_UINT32:  gguf_set_val_u32 (gguf_ctx, key.c_str(), rng());                         break;
+            case GGUF_TYPE_INT32:   gguf_set_val_i32 (gguf_ctx, key.c_str(), rng()             - (1 << 30)); break;
+            case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024      - 512);       break;
+            case GGUF_TYPE_BOOL:    gguf_set_val_bool(gguf_ctx, key.c_str(), rng() % 2 == 0);                break;
+            case GGUF_TYPE_STRING:  gguf_set_val_str (gguf_ctx, key.c_str(), std::to_string(rng()).c_str()); break;
+            case GGUF_TYPE_UINT64:  gguf_set_val_u64 (gguf_ctx, key.c_str(), rng());                         break;
+            case GGUF_TYPE_INT64:   gguf_set_val_i64 (gguf_ctx, key.c_str(), rng()             - (1 << 30)); break;
+            case GGUF_TYPE_FLOAT64: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024      - 512);       break;
+            case GGUF_TYPE_ARRAY: {
+                const enum gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT);
+                const uint64_t ne = rng() % 1024;
+
+                switch (type_arr) {
+                    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_BOOL:
+                    case GGUF_TYPE_UINT64:
+                    case GGUF_TYPE_INT64:
+                    case GGUF_TYPE_FLOAT64: {
+                        const size_t nbytes = ne*gguf_type_size(type_arr);
+                        std::vector<uint32_t> random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t));
+                        for (size_t j = 0; j < random_data.size(); ++j) {
+                            random_data[j] = rng();
+                            if (type_arr == GGUF_TYPE_BOOL) {
+                                random_data[j] &= 0x01010101; // the sanitizer complains if booleans are not 0 or 1
+                            }
+                        }
+                        gguf_set_arr_data(gguf_ctx, key.c_str(), type_arr, random_data.data(), ne);
+                    } break;
+                    case GGUF_TYPE_STRING: {
+                        std::vector<std::string>  data_cpp(ne);
+                        std::vector<const char *> data_c(ne);
+                        for (size_t j = 0; j < data_cpp.size(); ++j) {
+                            data_cpp[j] = std::to_string(rng());
+                            data_c[j]   = data_cpp[j].c_str();
+                        }
+                        gguf_set_arr_str(gguf_ctx, key.c_str(), data_c.data(), ne);
+                    } break;
+                    case GGUF_TYPE_ARRAY: {
+                        break; // not supported
+                    }
+                    case GGUF_TYPE_COUNT:
+                    default: {
+                        GGML_ABORT("fatal error");
+                    }
+                }
+            } break;
+            case GGUF_TYPE_COUNT:
+            default: {
+                GGML_ABORT("fatal error");
+            }
+        }
+    }
+
+    struct ggml_init_params ggml_params = {
+        /*.mem_size   =*/ 256*ggml_tensor_overhead(),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+    struct ggml_context * ctx = ggml_init(ggml_params);
+
+    for (int i = 0; i < 256; ++i) {
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT);
+        const size_t type_size = ggml_type_size(type);
+
+        if (type_size == 0) {
+            continue;
+        }
+
+        const int n_dims = 1 + rng() % GGML_MAX_DIMS;
+        int64_t ne[GGML_MAX_DIMS];
+        ne[0] = (1 + rng() % 10) * ggml_blck_size(type);
+        for (int j = 1; j < n_dims; ++j) {
+            ne[j] = 1 + rng() % 10;
+        }
+
+        struct ggml_tensor * tensor = ggml_new_tensor(ctx, type, n_dims, ne);
+        ggml_set_name(tensor, name.c_str());
+    }
+
+    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
+    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+        const size_t nbytes = ggml_nbytes(t);
+        std::vector<uint32_t> random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t));
+        for (size_t j = 0; j < random_data.size(); ++j) {
+            random_data[j] = rng();
+        }
+        ggml_backend_tensor_set(t, random_data.data(), 0, nbytes);
+
+        gguf_add_tensor(gguf_ctx, t);
+    }
+
+    return {gguf_ctx, ctx, buf};
+}
+
+static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other) {
+    bool ok = true;
+
+    const int n_kv = gguf_get_n_kv(ctx);
+    for (int id = 0; id < n_kv; ++id) {
+        const char * name = gguf_get_key(ctx, id);
+
+        const int idx_other = gguf_find_key(other, name);
+        if (idx_other < 0) {
+            ok = false;
+            continue;
+        }
+
+        const gguf_type type = gguf_get_kv_type(ctx, id);
+        if (type != gguf_get_kv_type(other, idx_other)) {
+            ok = false;
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const size_t arr_n = gguf_get_arr_n(ctx, id);
+            if (arr_n != gguf_get_arr_n(other, idx_other)) {
+                ok = false;
+                continue;
+            }
+
+            const gguf_type type_arr = gguf_get_arr_type(ctx, id);
+            if (type_arr != gguf_get_arr_type(other, idx_other)) {
+                ok = false;
+                continue;
+            }
+
+            if (type_arr == GGUF_TYPE_BOOL) {
+                const int8_t * data       = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx,   id));
+                const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    if (bool(data[arr_i]) != bool(data_other[arr_i])) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
+            if (type_arr == GGUF_TYPE_STRING) {
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    const std::string str       = gguf_get_arr_str(ctx,   id,       arr_i);
+                    const std::string str_other = gguf_get_arr_str(other, idx_other, arr_i);
+                    if (str != str_other) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
+            const int8_t * data       = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx,   id));
+            const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
+            if (!std::equal(data, data + arr_n*gguf_type_size(type_arr), data_other)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        if (type == GGUF_TYPE_STRING) {
+            const std::string str       = gguf_get_val_str(ctx,   id);
+            const std::string str_other = gguf_get_val_str(other, idx_other);
+            if (str != str_other) {
+                ok = false;
+            }
+            continue;
+        }
+
+        const char * data       = reinterpret_cast<const char *>(gguf_get_val_data(ctx,   id));
+        const char * data_other = reinterpret_cast<const char *>(gguf_get_val_data(other, idx_other));
+        if (!std::equal(data, data + gguf_type_size(type), data_other)) {
+            ok = false;
+        }
+    }
+
+    return ok;
+}
+
+static bool all_tensors_in_other(const gguf_context * ctx, const gguf_context * other) {
+    bool ok = true;
+
+    const int n_tensors = gguf_get_n_tensors(ctx);
+    for (int id = 0; id < n_tensors; ++id) {
+        const std::string name = gguf_get_tensor_name(ctx, id);
+
+        const int idx_other = gguf_find_tensor(other, name.c_str());
+        if (id != idx_other) {
+            ok = false;
+            if (idx_other < 0) {
+                continue;
+            }
+        }
+
+        const ggml_type type = gguf_get_tensor_type(ctx, id);
+        if (type != gguf_get_tensor_type(other, id)) {
+            ok = false;
+        }
+
+        const size_t offset = gguf_get_tensor_offset(ctx, id);
+        if (offset != gguf_get_tensor_offset(other, id)) {
+            ok = false;
+        }
+    }
+
+    return ok;
+}
+
+static bool same_tensor_data(const struct ggml_context * orig, const struct ggml_context * read) {
+    bool ok = true;
+
+    struct ggml_tensor * t_orig = ggml_get_first_tensor(orig);
+    struct ggml_tensor * t_read = ggml_get_first_tensor(read);
+
+    if (std::string(t_read->name) != "GGUF tensor data binary blob") {
+        return false;
+    }
+    t_read = ggml_get_next_tensor(read, t_read);
+
+    while (t_orig) {
+        if (!t_read) {
+            ok = false;
+            break;
+        }
+
+        const size_t nbytes = ggml_nbytes(t_orig);
+        if (ggml_nbytes(t_read) != nbytes) {
+            ok = false;
+            break;
+        }
+        std::vector<char> data_orig(nbytes);
+        ggml_backend_tensor_get(t_orig, data_orig.data(), 0, nbytes);
+        if (!std::equal(data_orig.data(), data_orig.data() + nbytes, reinterpret_cast<const char *>(t_read->data))) {
+            ok = false;
+        }
+
+        t_orig = ggml_get_next_tensor(orig, t_orig);
+        t_read = ggml_get_next_tensor(read, t_read);
+    }
+    if (t_read) {
+        ok = false;
+    }
+
+    return ok;
+}
+
+static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
+    ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+    printf("%s: device=%s, backend=%s, only_meta=%s\n",
+        __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend), only_meta ? "yes" : "no");
+
+    int npass = 0;
+    int ntest = 0;
+
+    struct gguf_context * gguf_ctx_0;
+    struct ggml_context * ctx_0;
+    ggml_backend_buffer_t bbuf;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed);
+        gguf_ctx_0 = result.gguf_ctx;
+        ctx_0      = result.ctx;
+        bbuf       = result.buffer;
+    }
+
+    FILE * file = tmpfile();
+
+#ifdef _WIN32
+    if (!file) {
+        printf("failed to create tmpfile(), needs elevated privileges on Windows");
+        printf("skipping tests");
+        return std::make_pair(0, 0);
+    }
+#else
+    GGML_ASSERT(file);
+#endif // _WIN32
+
+    {
+        std::vector<int8_t> buf;
+        gguf_write_to_buf(gguf_ctx_0, buf, only_meta);
+        GGML_ASSERT(fwrite(buf.data(), 1, buf.size(), file) == buf.size());
+        rewind(file);
+    }
+
+    struct ggml_context * ctx_1 = nullptr;
+    struct gguf_init_params gguf_params = {
+        /*no_alloc =*/ false,
+        /*ctx      =*/ only_meta ? nullptr : &ctx_1,
+    };
+    struct gguf_context * gguf_ctx_1 = gguf_init_from_file_impl(file, gguf_params);
+
+    printf("%s: same_version: ", __func__);
+    if (gguf_get_version(gguf_ctx_0) == gguf_get_version(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: same_n_kv: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: same_n_tensors: ", __func__);
+    if (gguf_get_n_tensors(gguf_ctx_0) == gguf_get_n_tensors(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_orig_kv_in_read: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_read_kv_in_orig: ", __func__);
+    if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_orig_tensors_in_read: ", __func__);
+    if (all_tensors_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_read_tensors_in_orig: ", __func__);
+    if (all_tensors_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    if (!only_meta) {
+        printf("%s: same_tensor_data: ", __func__);
+        if (same_tensor_data(ctx_0, ctx_1)) {
+            printf("\033[1;32mOK\033[0m\n");
+            npass++;
+        } else {
+            printf("\033[1;31mFAIL\033[0m\n");
+        }
+        ntest++;
+    }
+
+    ggml_backend_buffer_free(bbuf);
+    ggml_free(ctx_0);
+    ggml_free(ctx_1);
+    gguf_free(gguf_ctx_0);
+    gguf_free(gguf_ctx_1);
+    ggml_backend_free(backend);
+    fclose(file);
+
+    printf("\n");
+    return std::make_pair(npass, ntest);
+}
+
+static std::pair<int, int> test_gguf_set_kv(ggml_backend_dev_t dev, const unsigned int seed) {
+    ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+    printf("%s: device=%s, backend=%s\n", __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend));
+
+    int npass = 0;
+    int ntest = 0;
+
+    struct gguf_context * gguf_ctx_0;
+    struct ggml_context * ctx_0;
+    ggml_backend_buffer_t bbuf_0;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed);
+        gguf_ctx_0 = result.gguf_ctx;
+        ctx_0      = result.ctx;
+        bbuf_0     = result.buffer;
+    }
+
+    struct gguf_context * gguf_ctx_1;
+    struct ggml_context * ctx_1;
+    ggml_backend_buffer_t bbuf_1;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed + 1);
+        gguf_ctx_1 = result.gguf_ctx;
+        ctx_1      = result.ctx;
+        bbuf_1     = result.buffer;
+    }
+
+    struct gguf_context * gguf_ctx_2 = gguf_init_empty();
+
+    gguf_set_kv(gguf_ctx_1, gguf_ctx_0);
+    gguf_set_kv(gguf_ctx_2, gguf_ctx_0);
+
+    printf("%s: same_n_kv: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_2)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_0_in_1: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_0_in_2: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_2)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    gguf_set_kv(gguf_ctx_0, gguf_ctx_1);
+
+    printf("%s: same_n_kv_after_double_copy: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_1_in_0_after_double_copy: ", __func__);
+    if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    ggml_backend_buffer_free(bbuf_0);
+    ggml_backend_buffer_free(bbuf_1);
+    ggml_free(ctx_0);
+    ggml_free(ctx_1);
+    gguf_free(gguf_ctx_0);
+    gguf_free(gguf_ctx_1);
+    gguf_free(gguf_ctx_2);
+    ggml_backend_free(backend);
+
+    printf("\n");
+    return std::make_pair(npass, ntest);
+}
+
+static void print_usage() {
+    printf("usage: test-gguf [seed]\n");
+    printf("  if no seed is unspecified then a random seed is used\n");
+}
+
+int main(int argc, char ** argv) {
+    if (argc > 2) {
+        print_usage();
+        return 1;
+    }
+
+    std::random_device rd;
+    const unsigned int seed = argc < 2 ? rd() : std::stoi(argv[1]);
+
+    // Initialize ggml backends early so the prints aren't interleaved with the test results:
+    ggml_backend_dev_count();
+    fprintf(stderr, "\n");
+
+    int npass = 0;
+    int ntest = 0;
+    {
+        std::pair<int, int> result = test_handcrafted_file(seed);
+        npass += result.first;
+        ntest += result.second;
+    }
+
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+
+        for (bool only_meta : {true, false}) {
+            std::pair<int, int> result = test_roundtrip(dev, seed, only_meta);
+            npass += result.first;
+            ntest += result.second;
+        }
+
+        {
+            std::pair<int, int> result = test_gguf_set_kv(dev, seed);
+            npass += result.first;
+            ntest += result.second;
+        }
+    }
+
+    printf("%d/%d tests passed\n", npass, ntest);
+    if (npass != ntest) {
+        printf("\033[1;31mFAIL\033[0m\n");
+        return 1;
+    }
+    printf("\033[1;32mOK\033[0m\n");
+    return 0;
+}