--- /dev/null
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "../ggml/src/ggml-impl.h"
+
+#include <algorithm>
+#include <array>
+#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_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,
+ HANDCRAFTED_KV_DUPLICATE_KEY = 40 + 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_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_ALIGNMENT = 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_ALIGNMENT = 20 + offset_has_data,
+ HANDCRAFTED_DATA_SUCCESS = 800 + offset_has_data,
+ HANDCRAFTED_DATA_CUSTOM_ALIGN = 810 + offset_has_data,
+};
+
+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_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_BAD_VALUE_SIZE: return "KV_BAD_VALUE_SIZE";
+ case HANDCRAFTED_KV_DUPLICATE_KEY: return "KV_DUPLICATE_KEY";
+ 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_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_ALIGNMENT: return "TENSORS_BAD_ALIGNMENT";
+ 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_ALIGNMENT: return "DATA_BAD_ALIGNMENT";
+ 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;
+
+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;
+}
+
+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;
+}
+
+static void helper_write(const void * data, const size_t nbytes, FILE * file) {
+ 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();
+
+ std::mt19937 rng(seed);
+
+ if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) {
+ const char bad_magic[4] = {'F', 'U', 'G', 'G'};
+ helper_write(bad_magic, sizeof(bad_magic), file);
+ } else {
+ helper_write(GGUF_MAGIC, 4, file);
+ }
+
+ if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) {
+ const uint32_t version = 1;
+ helper_write(&version, sizeof(version), file);
+ } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) {
+ const uint32_t version = GGUF_VERSION + 1;
+ helper_write(&version, sizeof(version), file);
+ } else {
+ const uint32_t version = GGUF_VERSION;
+ helper_write(&version, sizeof(version), file);
+ }
+
+ 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(&n_tensors, sizeof(n_tensors), file);
+ } else {
+ const uint64_t n_tensors = tensor_configs.size();
+ helper_write(&n_tensors, sizeof(n_tensors), file);
+ }
+
+ 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_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+ n_kv += 1;
+ } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
+ n_kv = -1;
+ }
+ helper_write(&n_kv, sizeof(n_kv), file);
+ }
+
+ if (hft < offset_has_kv) {
+ for (int i = 0; i < extra_bytes; ++i) {
+ const char tmp = 0;
+ helper_write(&tmp, sizeof(tmp), file);
+ }
+ 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 ? -1 : kv_types[i].first);
+ const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : 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(&n, sizeof(n), file);
+ } else {
+ const uint64_t n = key.length();
+ helper_write(&n, sizeof(n), file);
+ }
+ helper_write(key.data(), key.length(), file);
+
+ {
+ const int32_t type32 = int32_t(type);
+ helper_write(&type32, sizeof(type32), file);
+ }
+
+ 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(&n, sizeof(n), file);
+ helper_write(data, n, file);
+ continue;
+ }
+
+ if (type == GGUF_TYPE_ARRAY) {
+ {
+ const int32_t type32 = int32_t(type_arr);
+ helper_write(&type32, sizeof(type32), file);
+ }
+ if (type_arr == GGUF_TYPE_STRING) {
+ const uint64_t nstr = rng() % (16 + 1);
+ helper_write(&nstr, sizeof(nstr), file);
+ for (uint64_t istr = 0; istr < nstr; ++istr) {
+ const uint64_t n = rng() % (sizeof(uint32_t) + 1);
+ helper_write(&n, sizeof(n), file);
+ helper_write(&data[istr], n, file);
+ }
+ continue;
+ }
+ const size_t type_size = gguf_type_size(type_arr);
+ const uint64_t n = (rng() % sizeof(data)) / type_size;
+ helper_write(&n, sizeof(n), file);
+ helper_write(&data, n*type_size, file);
+ continue;
+ }
+
+ size_t type_size = hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type);
+ if (hft == HANDCRAFTED_KV_BAD_VALUE_SIZE) {
+ type_size += rng() % 3;
+ }
+ helper_write(data, type_size, file);
+ }
+
+ if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+ const std::string key = "general.alignment";
+ {
+ const uint64_t n = key.length();
+ helper_write(&n, sizeof(n), file);
+ }
+ helper_write(key.data(), key.length(), file);
+
+ const int32_t type = gguf_type(GGUF_TYPE_UINT32);
+ helper_write(&type, sizeof(type), file);
+
+ const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT + 1;
+ helper_write(&alignment, sizeof(alignment), file);
+ }
+
+ if (hft < offset_has_tensors) {
+ for (int i = 0; i < extra_bytes; ++i) {
+ const char tmp = 0;
+ helper_write(&tmp, sizeof(tmp), file);
+ }
+ rewind(file);
+ return file;
+ }
+
+ uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
+ if (hft == HANDCRAFTED_TENSORS_BAD_ALIGNMENT || hft == HANDCRAFTED_DATA_BAD_ALIGNMENT) {
+ alignment -= 1;
+ } else if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+ alignment += 1;
+ }
+
+ uint64_t 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;
+
+ 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(&n, sizeof(n), file);
+ }
+ helper_write(name.data(), name.length(), file);
+
+ uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_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(&n_dims_bad, sizeof(n_dims_bad), file);
+ } else {
+ helper_write(&n_dims, sizeof(n_dims), file);
+ }
+
+ if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) {
+ for (uint32_t j = 0; j < n_dims; ++j) {
+ const int64_t bad_dim = -1;
+ helper_write(&bad_dim, sizeof(bad_dim), file);
+ }
+ } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
+ for (uint32_t j = 0; j < n_dims; ++j) {
+ const int64_t big_dim = 4*int64_t(INT32_MAX);
+ helper_write(&big_dim, sizeof(big_dim), file);
+ }
+ } else {
+ helper_write(shape.data(), n_dims*sizeof(int64_t), file);
+ }
+
+ {
+ const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? -1 : int32_t(type);
+ helper_write(&type32, sizeof(type32), file);
+ }
+
+ if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) {
+ const uint64_t bad_offset = -1;
+ helper_write(&bad_offset, sizeof(bad_offset), file);
+ } else {
+ helper_write(&offset, sizeof(offset), file);
+ }
+
+ 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);
+ }
+
+ const uint32_t alignment_overshoot = ftell(file) % alignment;
+ if (alignment_overshoot != 0) {
+ for (size_t i = alignment_overshoot; i < alignment; ++i) {
+ const char pad = 0;
+ helper_write(&pad, sizeof(pad), file);
+ }
+ }
+
+ 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(&random_byte, sizeof(random_byte), file);
+ }
+ }
+
+ for (int i = 0; i < extra_bytes; ++i) {
+ const char tmp = 0;
+ helper_write(&tmp, sizeof(tmp), file);
+ }
+ 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 (!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 (!std::equal(data8, data8 + gguf_type_size(type), data_gguf)) {
+ ok = false;
+ }
+ }
+
+ const uint32_t expected_alignment = alignment_defined ? GGUF_DEFAULT_ALIGNMENT + 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, "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;
+
+ const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
+
+ 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, size, file) == 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_1,
+ // HANDCRAFTED_FILE_TYPE_BAD_VERSION_FUTURE, // FIXME
+ HANDCRAFTED_HEADER_BAD_N_KV,
+ HANDCRAFTED_HEADER_BAD_N_TENSORS,
+ HANDCRAFTED_HEADER_EMPTY,
+
+ HANDCRAFTED_KV_BAD_KEY_SIZE,
+ HANDCRAFTED_KV_BAD_TYPE,
+ HANDCRAFTED_KV_BAD_VALUE_SIZE,
+ // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME
+ HANDCRAFTED_KV_SUCCESS,
+
+ HANDCRAFTED_TENSORS_BAD_NAME_SIZE,
+ HANDCRAFTED_TENSORS_BAD_N_DIMS,
+ HANDCRAFTED_TENSORS_BAD_SHAPE,
+ HANDCRAFTED_TENSORS_NE_TOO_BIG,
+ HANDCRAFTED_TENSORS_BAD_TYPE,
+ // HANDCRAFTED_TENSORS_BAD_OFFSET, // FIXME
+ HANDCRAFTED_TENSORS_DUPLICATE_NAME,
+ // HANDCRAFTED_TENSORS_BAD_ALIGNMENT, // FIXME
+ HANDCRAFTED_TENSORS_SUCCESS,
+ HANDCRAFTED_TENSORS_CUSTOM_ALIGN,
+
+ HANDCRAFTED_DATA_NOT_ENOUGH_DATA,
+ // HANDCRAFTED_DATA_BAD_ALIGNMENT, // FIXME
+ 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("%s: failed to create tmpfile(), needs elevated privileges on Windows");
+ printf("%s: 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 (false && hft >= offset_has_data && !expect_context_not_null(hft)) { // FIXME
+ 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++;
+ }
+
+ if (false && expect_context_not_null(hft)) { // FIXME
+ FILE * file_eb = get_handcrafted_file(seed, hft, /*extra_bytes =*/ 1);
+ struct gguf_context * gguf_ctx_eb = gguf_init_from_file_impl(file_eb, gguf_params);
+
+ printf("%s: - context_null_with_extra_bytes: ", __func__);
+ if (gguf_ctx_eb) {
+ printf("\033[1;31mFAIL\033[0m\n");
+ } else {
+ printf("\033[1;32mOK\033[0m\n");
+ npass++;
+ }
+ ntest++;
+
+ gguf_free(gguf_ctx_eb);
+ fclose(file_eb);
+ }
+
+ 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++;
+ }
+
+ if (gguf_ctx) {
+ ggml_free(ctx);
+ gguf_free(gguf_ctx);
+ }
+ fclose(file);
+ 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);
+
+ if (type == GGUF_TYPE_STRING || type == GGUF_TYPE_ARRAY) {
+ continue; // FIXME memory leak
+ }
+
+ 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();
+ }
+ 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;
+ }
+ } break;
+ case GGUF_TYPE_COUNT:
+ default: {
+ GGML_ABORT("fatal error");
+ } break;
+ }
+ }
+
+ 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 int 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_STRING) {
+ for (int 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 char * data = reinterpret_cast<const char *>(gguf_get_arr_data(ctx, id));
+ const char * data_other = reinterpret_cast<const char *>(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);
+ 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(orig, t_read);
+ }
+ if (t_read) {
+ ok = false;
+ }
+
+ return true;
+}
+
+static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
+ FILE * file = tmpfile();
+#ifdef _WIN32
+ if (!file) {
+ printf("%s: failed to create tmpfile(), needs elevated privileges on Windows");
+ printf("%s: skipping tests");
+ return std::make_pair(0, 0);
+ }
+#else
+ GGML_ASSERT(file);
+#endif // _WIN32
+
+ if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
+ return std::make_pair(0, 0); // FIXME
+ }
+
+ 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;
+ }
+
+ struct gguf_buf gbuf = gguf_buf_init(16 * 1024);
+ gguf_write_to_buf(gguf_ctx_0, &gbuf, only_meta);
+ helper_write(gbuf.data, gbuf.offset, file);
+ 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);
+ gguf_buf_free(gbuf);
+ ggml_backend_free(backend);
+ GGML_ASSERT(fclose(file) == 0);
+
+ 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;
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff