mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-28 08:31:25 +00:00
1339 lines
45 KiB
C++
1339 lines
45 KiB
C++
#include "ggml.h"
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#include "ggml-backend.h"
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#include "../ggml/src/ggml-impl.h"
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#include <algorithm>
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#include <array>
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#include <cstdint>
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#include <cstdio>
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#include <random>
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#include <string>
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#include <vector>
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constexpr int offset_has_kv = 1000;
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constexpr int offset_has_tensors = 2000;
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constexpr int offset_has_data = 3000;
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enum handcrafted_file_type {
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HANDCRAFTED_HEADER_BAD_MAGIC = 10,
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HANDCRAFTED_HEADER_BAD_VERSION_1 = 20,
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HANDCRAFTED_HEADER_BAD_VERSION_FUTURE = 30,
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HANDCRAFTED_HEADER_BAD_N_TENSORS = 40,
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HANDCRAFTED_HEADER_BAD_N_KV = 50,
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HANDCRAFTED_HEADER_EMPTY = 800,
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HANDCRAFTED_KV_BAD_KEY_SIZE = 10 + offset_has_kv,
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HANDCRAFTED_KV_BAD_TYPE = 20 + offset_has_kv,
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// HANDCRAFTED_KV_BAD_VALUE_SIZE = 30 + offset_has_kv, // removed because it can result in allocations > 1 TB (default sanitizer limit)
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HANDCRAFTED_KV_DUPLICATE_KEY = 40 + offset_has_kv,
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HANDCRAFTED_KV_BAD_ALIGN = 50 + offset_has_kv,
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HANDCRAFTED_KV_SUCCESS = 800 + offset_has_kv,
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HANDCRAFTED_TENSORS_BAD_NAME_SIZE = 10 + offset_has_tensors,
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HANDCRAFTED_TENSORS_BAD_N_DIMS = 20 + offset_has_tensors,
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HANDCRAFTED_TENSORS_BAD_SHAPE = 30 + offset_has_tensors,
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HANDCRAFTED_TENSORS_NE_TOO_BIG = 40 + offset_has_tensors,
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HANDCRAFTED_TENSORS_BAD_TYPE = 50 + offset_has_tensors,
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HANDCRAFTED_TENSORS_BAD_OFFSET = 60 + offset_has_tensors,
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HANDCRAFTED_TENSORS_DUPLICATE_NAME = 70 + offset_has_tensors,
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HANDCRAFTED_TENSORS_BAD_ALIGN = 75 + offset_has_tensors,
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HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN = 80 + offset_has_tensors,
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HANDCRAFTED_TENSORS_SUCCESS = 800 + offset_has_tensors,
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HANDCRAFTED_TENSORS_CUSTOM_ALIGN = 810 + offset_has_tensors,
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HANDCRAFTED_DATA_NOT_ENOUGH_DATA = 10 + offset_has_data,
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HANDCRAFTED_DATA_BAD_ALIGN = 15 + offset_has_data,
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HANDCRAFTED_DATA_INCONSISTENT_ALIGN = 20 + offset_has_data,
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HANDCRAFTED_DATA_SUCCESS = 800 + offset_has_data,
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HANDCRAFTED_DATA_CUSTOM_ALIGN = 810 + offset_has_data,
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};
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static std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
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switch (hft) {
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case HANDCRAFTED_HEADER_BAD_MAGIC: return "HEADER_BAD_MAGIC";
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case HANDCRAFTED_HEADER_BAD_VERSION_1: return "HEADER_BAD_VERSION_1";
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case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE: return "HEADER_BAD_VERSION_FUTURE";
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case HANDCRAFTED_HEADER_BAD_N_KV: return "HEADER_BAD_N_KV";
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case HANDCRAFTED_HEADER_BAD_N_TENSORS: return "HEADER_BAD_N_TENSORS";
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case HANDCRAFTED_HEADER_EMPTY: return "HEADER_EMPTY";
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case HANDCRAFTED_KV_BAD_KEY_SIZE: return "KV_BAD_KEY_SIZE";
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case HANDCRAFTED_KV_BAD_TYPE: return "KV_BAD_TYPE";
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case HANDCRAFTED_KV_DUPLICATE_KEY: return "KV_DUPLICATE_KEY";
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case HANDCRAFTED_KV_BAD_ALIGN: return "KV_BAD_ALIGN";
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case HANDCRAFTED_KV_SUCCESS: return "KV_RANDOM_KV";
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case HANDCRAFTED_TENSORS_BAD_NAME_SIZE: return "TENSORS_BAD_NAME_SIZE";
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case HANDCRAFTED_TENSORS_BAD_N_DIMS: return "TENSORS_BAD_N_DIMS";
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case HANDCRAFTED_TENSORS_BAD_SHAPE: return "TENSORS_BAD_SHAPE";
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case HANDCRAFTED_TENSORS_NE_TOO_BIG: return "TENSORS_NE_TOO_BIG";
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case HANDCRAFTED_TENSORS_BAD_TYPE: return "TENSORS_BAD_TYPE";
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case HANDCRAFTED_TENSORS_BAD_OFFSET: return "TENSORS_BAD_OFFSET";
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case HANDCRAFTED_TENSORS_DUPLICATE_NAME: return "TENSORS_DUPLICATE_NAME";
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case HANDCRAFTED_TENSORS_BAD_ALIGN: return "TENSORS_BAD_ALIGN";
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case HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN: return "TENSORS_INCONSISTENT_ALIGN";
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case HANDCRAFTED_TENSORS_SUCCESS: return "TENSORS_SUCCESS";
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case HANDCRAFTED_TENSORS_CUSTOM_ALIGN: return "TENSORS_CUSTOM_ALIGN";
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case HANDCRAFTED_DATA_NOT_ENOUGH_DATA: return "DATA_NOT_ENOUGH_DATA";
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case HANDCRAFTED_DATA_BAD_ALIGN: return "DATA_BAD_ALIGN";
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case HANDCRAFTED_DATA_INCONSISTENT_ALIGN: return "DATA_INCONSISTENT_ALIGN";
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case HANDCRAFTED_DATA_SUCCESS: return "DATA_SUCCESS";
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case HANDCRAFTED_DATA_CUSTOM_ALIGN: return "DATA_CUSTOM_ALIGN";
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}
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GGML_ABORT("fatal error");
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}
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static bool expect_context_not_null(const enum handcrafted_file_type hft) {
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if (hft < offset_has_kv) {
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return hft >= HANDCRAFTED_HEADER_EMPTY;
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}
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if (hft < offset_has_tensors) {
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return hft >= HANDCRAFTED_KV_SUCCESS;
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}
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if (hft < offset_has_data) {
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return hft >= HANDCRAFTED_TENSORS_SUCCESS;
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}
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return hft >= HANDCRAFTED_DATA_SUCCESS;
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}
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typedef std::pair<enum ggml_type, std::array<int64_t, GGML_MAX_DIMS>> tensor_config_t;
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static std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
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std::vector<tensor_config_t> tensor_configs;
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tensor_configs.reserve(100);
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for (int i = 0; i < 100; ++i) {
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const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT);
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if (ggml_type_size(type) == 0) {
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continue;
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}
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std::array<int64_t, GGML_MAX_DIMS> shape = {1, 1, 1, 1};
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shape[0] = (1 + rng() % 10) * ggml_blck_size(type);
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const int n_dims = 1 + rng() % GGML_MAX_DIMS;
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for (int i = 1; i < n_dims; ++i) {
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shape[i] = 1 + rng() % 10;
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}
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tensor_configs.push_back(std::make_pair(type, shape));
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}
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return tensor_configs;
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}
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static std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
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std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
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kv_types.reserve(100);
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for (int i = 0; i < 100; ++i) {
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const gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
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if (type == GGUF_TYPE_ARRAY) {
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const gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT);
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if (type_arr == GGUF_TYPE_ARRAY) {
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continue;
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}
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kv_types.push_back(std::make_pair(type, type_arr));
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continue;
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}
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kv_types.push_back(std::make_pair(type, gguf_type(-1)));
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}
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std::shuffle(kv_types.begin(), kv_types.end(), rng);
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return kv_types;
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}
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template <typename T>
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static void helper_write(FILE * file, const T & val) {
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GGML_ASSERT(fwrite(&val, 1, sizeof(val), file) == sizeof(val));
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}
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static void helper_write(FILE * file, const void * data, const size_t nbytes) {
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GGML_ASSERT(fwrite(data, 1, nbytes, file) == nbytes);
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}
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static FILE * get_handcrafted_file(const unsigned int seed, const enum handcrafted_file_type hft, const int extra_bytes = 0) {
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FILE * file = tmpfile();
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if (!file) {
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return file;
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}
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std::mt19937 rng(seed);
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uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
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if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) {
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const char bad_magic[4] = {'F', 'U', 'G', 'G'};
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helper_write(file, bad_magic, sizeof(bad_magic));
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} else {
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helper_write(file, GGUF_MAGIC, 4);
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}
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if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) {
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const uint32_t version = 1;
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helper_write(file, version);
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} else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) {
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const uint32_t version = GGUF_VERSION + 1;
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helper_write(file, version);
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} else {
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const uint32_t version = GGUF_VERSION;
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helper_write(file, version);
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}
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std::vector<tensor_config_t> tensor_configs;
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if (hft >= offset_has_tensors) {
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tensor_configs = get_tensor_configs(rng);
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}
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if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) {
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const uint64_t n_tensors = -1;
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helper_write(file, n_tensors);
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} else {
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const uint64_t n_tensors = tensor_configs.size();
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helper_write(file, n_tensors);
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}
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std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
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if (hft >= offset_has_kv) {
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kv_types = get_kv_types(rng);
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}
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{
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uint64_t n_kv = kv_types.size();
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if (hft == HANDCRAFTED_KV_BAD_ALIGN ||
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hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
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hft == HANDCRAFTED_DATA_BAD_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
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n_kv += 1;
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} else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
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n_kv = -1;
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}
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helper_write(file, n_kv);
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}
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if (hft < offset_has_kv) {
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while (ftell(file) % alignment != 0) {
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const char pad = 0;
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helper_write(file, pad);
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}
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for (int i = 0; i < extra_bytes; ++i) {
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const char tmp = 0;
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helper_write(file, tmp);
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}
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rewind(file);
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return file;
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}
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for (int i = 0; i < int(kv_types.size()); ++i) {
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const enum gguf_type type = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].first);
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const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].second);
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const std::string key = "my_key_" + std::to_string((hft == HANDCRAFTED_KV_DUPLICATE_KEY ? i/2 : i));
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if (hft == HANDCRAFTED_KV_BAD_KEY_SIZE) {
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const uint64_t n = -1;
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helper_write(file, n);
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} else {
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const uint64_t n = key.length();
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helper_write(file, n);
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}
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helper_write(file, key.data(), key.length());
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{
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const int32_t type32 = int32_t(type);
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helper_write(file, type32);
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}
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uint32_t data[16];
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for (int j = 0; j < 16; ++j) {
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data[j] = rng();
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if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) {
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data[j] |= 0x01010101; // avoid random null-termination of string
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}
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}
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if (type == GGUF_TYPE_STRING) {
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const uint64_t n = rng() % sizeof(data);
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helper_write(file, n);
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helper_write(file, data, n);
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continue;
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}
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if (type == GGUF_TYPE_ARRAY) {
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{
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const int32_t type32 = int32_t(type_arr);
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helper_write(file, type32);
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}
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if (type_arr == GGUF_TYPE_STRING) {
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const uint64_t nstr = rng() % (16 + 1);
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helper_write(file, nstr);
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for (uint64_t istr = 0; istr < nstr; ++istr) {
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const uint64_t n = rng() % (sizeof(uint32_t) + 1);
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helper_write(file, n);
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helper_write(file, &data[istr], n);
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}
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continue;
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}
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const size_t type_size = gguf_type_size(type_arr);
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const uint64_t n = (rng() % sizeof(data)) / type_size;
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helper_write(file, n);
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helper_write(file, &data, n*type_size);
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continue;
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}
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helper_write(file, data, hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type));
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}
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if (hft == HANDCRAFTED_KV_BAD_ALIGN ||
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hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
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hft == HANDCRAFTED_DATA_BAD_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
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const uint64_t n = strlen(GGUF_KEY_GENERAL_ALIGNMENT);
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helper_write(file, n);
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helper_write(file, GGUF_KEY_GENERAL_ALIGNMENT, n);
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const int32_t type = gguf_type(GGUF_TYPE_UINT32);
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helper_write(file, type);
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alignment = expect_context_not_null(hft) ? 1 : 13;
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helper_write(file, alignment);
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}
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if (hft < offset_has_tensors) {
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while (ftell(file) % alignment != 0) {
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const char pad = 0;
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helper_write(file, pad);
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}
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for (int i = 0; i < extra_bytes; ++i) {
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const char tmp = 0;
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helper_write(file, tmp);
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}
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rewind(file);
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return file;
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}
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if (hft == HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN || hft == HANDCRAFTED_DATA_INCONSISTENT_ALIGN) {
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alignment = 1;
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}
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uint64_t offset = 0;
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for (int i = 0; i < int(tensor_configs.size()); ++i) {
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const ggml_type type = tensor_configs[i].first;
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const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
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std::string name = "my_tensor";
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if (hft != HANDCRAFTED_TENSORS_DUPLICATE_NAME) {
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name += "_" + std::to_string(i);
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}
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if (hft == HANDCRAFTED_TENSORS_BAD_NAME_SIZE) {
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name += "_with_a_very_long_name_which_is_longer_than_what_is_allowed_for_ggml_tensors";
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GGML_ASSERT(name.length() >= GGML_MAX_NAME);
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}
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{
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const uint64_t n = name.length();
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helper_write(file, n);
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}
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helper_write(file, name.data(), name.length());
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uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_TOO_BIG ? 2 : 1;
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for (int i = GGML_MAX_DIMS-1; i >= 1; --i) {
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if (shape[i] != 1) {
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n_dims = i + 1;
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break;
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}
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}
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if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) {
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const uint32_t n_dims_bad = GGML_MAX_DIMS + 1;
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helper_write(file, n_dims_bad);
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} else {
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helper_write(file, n_dims);
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}
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if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) {
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for (uint32_t j = 0; j < n_dims; ++j) {
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const int64_t bad_dim = -1;
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helper_write(file, bad_dim);
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}
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} else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
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for (uint32_t j = 0; j < n_dims; ++j) {
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const int64_t big_dim = 4*int64_t(INT32_MAX);
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helper_write(file, big_dim);
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}
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} else {
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helper_write(file, shape.data(), n_dims*sizeof(int64_t));
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}
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{
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const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? GGML_TYPE_COUNT : int32_t(type);
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helper_write(file, type32);
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}
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if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) {
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const uint64_t bad_offset = -1;
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helper_write(file, bad_offset);
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} else {
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helper_write(file, offset);
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}
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int64_t ne = shape[0];
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for (uint32_t i = 1; i < n_dims; ++i) {
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ne *= shape[i];
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}
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offset += GGML_PAD(ggml_row_size(type, ne), alignment);
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}
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while (ftell(file) % alignment != 0) {
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const char pad = 0;
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helper_write(file, pad);
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}
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if (hft >= offset_has_data) {
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rng.seed(seed + 1);
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uint64_t nbytes = offset;
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if (hft == HANDCRAFTED_DATA_NOT_ENOUGH_DATA) {
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nbytes -= 1;
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}
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for (uint64_t i = 0; i < nbytes; ++i) {
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const uint8_t random_byte = i % 256;
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helper_write(file, random_byte);
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}
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}
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for (int i = 0; i < extra_bytes; ++i) {
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const char tmp = 0;
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helper_write(file, tmp);
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}
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rewind(file);
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return file;
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}
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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) {
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if (!gguf_ctx) {
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return false;
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}
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std::mt19937 rng(seed);
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std::vector<tensor_config_t> tensor_configs;
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if (has_tensors) {
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tensor_configs = get_tensor_configs(rng);
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}
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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_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_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;
|
|
}
|