cmake : add sanitizer flags for llama.cpp (#11279)
* cmake : add sanitizer flags for llama.cpp ggml-ci * tests : fix compile warnings ggml-ci * cmake : move sanitizer flags to llama_add_compile_flags ggml-ci * cmake : move llama.cpp compile flags to top level lists ggml-ci * cmake : apply only sanitizer flags at top level ggml-ci * tests : fix gguf context use in same_tensor_data * gguf-test: tensor data comparison * dummy : trigger ggml-ci * unicode : silence gcc warnings ggml-ci * ci : use sanitizer builds only in Debug mode ggml-ci * cmake : add status messages [no ci] --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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7 changed files with 74 additions and 41 deletions
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@ -48,7 +48,7 @@ enum handcrafted_file_type {
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HANDCRAFTED_DATA_CUSTOM_ALIGN = 810 + offset_has_data,
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};
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std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
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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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@ -99,7 +99,7 @@ static bool expect_context_not_null(const enum handcrafted_file_type hft) {
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typedef std::pair<enum ggml_type, std::array<int64_t, GGML_MAX_DIMS>> tensor_config_t;
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std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
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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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@ -122,7 +122,7 @@ std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
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return tensor_configs;
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}
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std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
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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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@ -626,8 +626,6 @@ static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const u
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bool ok = true;
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const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
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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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@ -866,13 +864,13 @@ static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t
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case GGUF_TYPE_COUNT:
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default: {
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GGML_ABORT("fatal error");
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} break;
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}
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}
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} break;
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case GGUF_TYPE_COUNT:
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default: {
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GGML_ABORT("fatal error");
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} break;
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}
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}
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}
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@ -938,7 +936,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
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}
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if (type == GGUF_TYPE_ARRAY) {
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const int arr_n = gguf_get_arr_n(ctx, id);
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const size_t arr_n = gguf_get_arr_n(ctx, id);
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if (arr_n != gguf_get_arr_n(other, idx_other)) {
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ok = false;
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continue;
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@ -953,7 +951,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
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if (type_arr == GGUF_TYPE_BOOL) {
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const int8_t * data = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx, id));
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const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
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for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
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for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
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if (bool(data[arr_i]) != bool(data_other[arr_i])) {
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ok = false;
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}
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@ -962,7 +960,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
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}
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if (type_arr == GGUF_TYPE_STRING) {
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for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
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for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
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const std::string str = gguf_get_arr_str(ctx, id, arr_i);
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const std::string str_other = gguf_get_arr_str(other, idx_other, arr_i);
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if (str != str_other) {
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@ -1033,6 +1031,12 @@ static bool same_tensor_data(const struct ggml_context * orig, const struct ggml
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struct ggml_tensor * t_orig = ggml_get_first_tensor(orig);
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struct ggml_tensor * t_read = ggml_get_first_tensor(read);
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if (std::string(t_read->name) != "GGUF tensor data binary blob") {
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return false;
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}
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t_read = ggml_get_next_tensor(read, t_read);
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while (t_orig) {
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if (!t_read) {
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ok = false;
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@ -1051,13 +1055,13 @@ static bool same_tensor_data(const struct ggml_context * orig, const struct ggml
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}
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t_orig = ggml_get_next_tensor(orig, t_orig);
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t_read = ggml_get_next_tensor(orig, t_read);
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t_read = ggml_get_next_tensor(read, t_read);
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}
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if (t_read) {
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ok = false;
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}
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return true;
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return ok;
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}
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static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
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