vbatts/llama.cpp
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fix some warnings from gcc and clang-tidy (#3038)

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Co-authored-by: xaedes <xaedes@gmail.com>
This commit is contained in:
Cebtenzzre 2023-09-07 13:22:29 -04:00 • committed by GitHub
parent 4fa2cc1750
commit 00d62adb79
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GPG key ID: 4AEE18F83AFDEB23
22 changed files with 63 additions and 101 deletions
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8
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -1,5 +1,6 @@
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include <unordered_map>
#include <vector>
@ -499,10 +500,10 @@ struct llama_file {
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
die_fmt("fread failed: %s", strerror(errno));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
die("unexpectedly reached end of file");
}
}
@ -597,8 +598,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab)
printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
llama_file file(filename, "rb");
if (!file.fp) {
fprintf(stderr, "error: %s: %s\n", strerror(errno), filename);
exit(1);
die_fmt("%s: %s", strerror(errno), filename);
}
const int n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused

2
examples/embd-input/embd-input-lib.cpp
View file

@ -23,7 +23,7 @@ extern "C" {
struct MyModel* create_mymodel(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return nullptr;
}

2
examples/embedding/embedding.cpp
View file

@ -11,7 +11,7 @@
int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}

2
examples/gptneox-wip/falcon-main.cpp
View file

@ -953,7 +953,7 @@ int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}

2
examples/gptneox-wip/gptneox-main.cpp
View file

@ -925,7 +925,7 @@ int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}

19
examples/main/main.cpp
View file

@ -48,8 +48,9 @@ static bool is_interacting = false;
void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
if (params.logdir.empty()) {
return;
}
@ -109,7 +110,7 @@ int main(int argc, char ** argv) {
gpt_params params;
g_params = &params;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
@ -303,7 +304,7 @@ int main(int argc, char ** argv) {
// debug message about similarity of saved session, if applicable
size_t n_matching_session_tokens = 0;
if (session_tokens.size() > 0) {
if (!session_tokens.empty()) {
for (llama_token id : session_tokens) {
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
break;
@ -401,7 +402,7 @@ int main(int argc, char ** argv) {
LOG_TEE("%s: interactive mode on.\n", __func__);
if (params.antiprompt.size()) {
if (!params.antiprompt.empty()) {
for (const auto & antiprompt : params.antiprompt) {
LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
}
@ -499,7 +500,7 @@ int main(int argc, char ** argv) {
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (embd.size() > 0) {
if (!embd.empty()) {
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
// --prompt or --file which uses the same value.
int max_embd_size = n_ctx - 4;
@ -624,7 +625,7 @@ int main(int argc, char ** argv) {
LOG("n_past = %d\n", n_past);
}
if (embd.size() > 0 && !path_session.empty()) {
if (!embd.empty() && !path_session.empty()) {
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
n_session_consumed = session_tokens.size();
}
@ -695,7 +696,7 @@ int main(int argc, char ** argv) {
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// check for reverse prompt
if (params.antiprompt.size()) {
if (!params.antiprompt.empty()) {
std::string last_output;
for (auto id : last_tokens) {
last_output += llama_token_to_piece(ctx, id);
@ -732,7 +733,7 @@ int main(int argc, char ** argv) {
LOG("found EOS token\n");
if (params.interactive) {
if (params.antiprompt.size() != 0) {
if (!params.antiprompt.empty()) {
// tokenize and inject first reverse prompt
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());

2
examples/perplexity/perplexity.cpp
View file

@ -655,7 +655,7 @@ int main(int argc, char ** argv) {
gpt_params params;
params.n_batch = 512;
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}

2
examples/quantize-stats/quantize-stats.cpp
View file

@ -71,7 +71,7 @@ void quantize_stats_print_usage(int /*argc*/, char ** argv) {
}
// Check if a layer is included/excluded by command line
bool layer_included(const quantize_stats_params params, const std::string & layer) {
bool layer_included(const quantize_stats_params & params, const std::string & layer) {
for (const auto& excluded : params.exclude_layers) {
if (std::regex_search(layer, std::regex(excluded))) {
return false;

7
examples/quantize/quantize.cpp
View file

@ -143,10 +143,9 @@ int main(int argc, char ** argv) {
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
return 1;
} else {
if (ftype_str == "COPY") {
params.only_copy = true;
}
}
if (ftype_str == "COPY") {
params.only_copy = true;
}
arg_idx++;
}

4
examples/save-load-state/save-load-state.cpp
View file

@ -13,7 +13,7 @@ int main(int argc, char ** argv) {
params.repeat_last_n = 64;
params.prompt = "The quick brown fox";
if (gpt_params_parse(argc, argv, params) == false) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
@ -44,7 +44,7 @@ int main(int argc, char ** argv) {
llama_free_model(model);
return 1;
}
auto tokens = llama_tokenize(ctx, params.prompt.c_str(), true);
auto tokens = llama_tokenize(ctx, params.prompt, true);
auto n_prompt_tokens = tokens.size();
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);

8
examples/server/server.cpp
View file

@ -139,7 +139,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c
}
// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> probs)
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> & probs)
{
json out = json::array();
for (const auto &prob : probs)
@ -271,7 +271,7 @@ struct llama_server_context
return true;
}
std::vector<llama_token> tokenize(json json_prompt, bool add_bos)
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
{
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
@ -611,7 +611,7 @@ struct llama_server_context
completion_token_output doCompletion()
{
const completion_token_output token_with_probs = nextToken();
auto token_with_probs = nextToken();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
generated_text += token_text;
@ -1255,7 +1255,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
struct token_translator {
llama_context * ctx;
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); }
};
void append_to_generated_text_from_generated_token_probs(llama_server_context & llama) {

46
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -169,10 +169,6 @@ struct my_llama_hparams {
float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f;
bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(my_llama_hparams));
}
};
struct my_llama_layer {
@ -929,28 +925,6 @@ void get_example_targets_batch(struct llama_context * lctx, const int * train_sa
}
}
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
int tokenize_file(struct llama_context * lctx, const char * filename, std::vector<llama_token>& out) {
FILE * fp = std::fopen(filename, "rb");
if (fp == NULL) {
@ -983,10 +957,10 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
out.resize(size+1);
if (std::fread(buf.data(), size, 1, fp) != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
die("unexpectedly reached end of file");
}
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
die_fmt("fread failed: %s", strerror(errno));
}
buf[size] = '\0';
@ -1047,11 +1021,11 @@ void shuffle_ints(int * begin, int * end) {
if (kid >= 0) { \
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
if (ktype != (type)) { \
throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
} \
(dst) = func(ctx, kid); \
} else if (req) { \
throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
die_fmt("key not found in model: %s", skey.c_str()); \
} \
}
@ -1136,7 +1110,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
read_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
read_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
} else {
throw std::runtime_error("unknown optimizer type\n");
die("unknown optimizer type");
}
}
@ -1315,20 +1289,20 @@ void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_mod
const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
if (token_idx == -1) {
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
die("cannot find tokenizer vocab in model file");
}
const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
if (score_idx == -1) {
throw std::runtime_error("cannot find tokenizer scores in model file\n");
die("cannot find tokenizer scores in model file");
}
const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
if (toktype_idx == -1) {
throw std::runtime_error("cannot find token type list in GGUF file\n");
die("cannot find token type list in GGUF file");
}
const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
@ -1356,7 +1330,7 @@ void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_mod
// read and copy bpe merges
const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
if (merges_keyidx == -1) {
throw std::runtime_error("cannot find tokenizer merges in model file\n");
die("cannot find tokenizer merges in model file");
}
const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
@ -1988,7 +1962,7 @@ void opt_callback(void * vdata, float * sched) {
float min_sched = params->adam_min_alpha / params->adam_alpha;
*sched = min_sched + *sched * (1.0f - min_sched);
int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
int impr_plot = std::isnan(opt->loss_after) ? 0 : -std::lround(1 + (opt->loss_before - opt->loss_after) * 10.0f);
printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0);
if (data->shuffle_countdown < n_batch) {