From 3aefaab9e59335ebb07d5205dbc8633efd680e58 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 15 Sep 2023 15:38:27 -0400 Subject: [PATCH] check C++ code with -Wmissing-declarations (#3184) --- CMakeLists.txt | 1 + Makefile | 13 +- common/common.cpp | 2 +- common/console.cpp | 18 +-- common/grammar-parser.cpp | 30 ++-- examples/baby-llama/baby-llama.cpp | 148 +++++++++--------- examples/beam-search/beam-search.cpp | 7 +- .../convert-llama2c-to-ggml.cpp | 38 ++--- examples/gguf/gguf.cpp | 8 +- examples/main/main.cpp | 5 +- examples/perplexity/perplexity.cpp | 30 ++-- examples/quantize-stats/quantize-stats.cpp | 49 ++---- examples/quantize/quantize.cpp | 4 +- examples/server/server.cpp | 12 +- llama.cpp | 19 ++- llama.h | 4 +- pocs/vdot/vdot.cpp | 5 +- tests/test-opt.cpp | 21 ++- tests/test-quantize-fns.cpp | 26 +-- tests/test-quantize-perf.cpp | 10 +- tests/test-sampling.cpp | 38 ++--- tests/test-tokenizer-1-llama.cpp | 2 +- 22 files changed, 247 insertions(+), 243 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3283f7b65..abecd684b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -427,6 +427,7 @@ if (LLAMA_ALL_WARNINGS) -Wextra -Wpedantic -Wcast-qual + -Wmissing-declarations -Wno-unused-function -Wno-multichar ) diff --git a/Makefile b/Makefile index 7ab1b7a09..98bf8845c 100644 --- a/Makefile +++ b/Makefile @@ -172,9 +172,16 @@ endif # LLAMA_DISABLE_LOGS # warnings MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function -MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wmissing-declarations -Wno-unused-function -Wno-multichar -ifeq '' '$(findstring clang,$(shell $(CXX) --version))' +# TODO(cebtenzzre): remove this once PR #2632 gets merged +TTFS_CXXFLAGS = $(CXXFLAGS) -Wno-missing-declarations + +ifneq '' '$(findstring clang,$(shell $(CXX) --version))' + # clang++ only + MK_CXXFLAGS += -Wmissing-prototypes + TTFS_CXXFLAGS += -Wno-missing-prototypes +else # g++ only MK_CXXFLAGS += -Wno-format-truncation -Wno-array-bounds endif @@ -524,7 +531,7 @@ gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(TTFS_CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/common/common.cpp b/common/common.cpp index 9969cb97d..02ec0f8d0 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -78,7 +78,7 @@ int32_t get_num_physical_cores() { return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; } -void process_escapes(std::string& input) { +static void process_escapes(std::string& input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; diff --git a/common/console.cpp b/common/console.cpp index 23545e5be..f65cbc6ed 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -158,7 +158,7 @@ namespace console { } } - char32_t getchar32() { + static char32_t getchar32() { #if defined(_WIN32) HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE); wchar_t high_surrogate = 0; @@ -212,7 +212,7 @@ namespace console { #endif } - void pop_cursor() { + static void pop_cursor() { #if defined(_WIN32) if (hConsole != NULL) { CONSOLE_SCREEN_BUFFER_INFO bufferInfo; @@ -233,7 +233,7 @@ namespace console { putc('\b', out); } - int estimateWidth(char32_t codepoint) { + static int estimateWidth(char32_t codepoint) { #if defined(_WIN32) (void)codepoint; return 1; @@ -242,7 +242,7 @@ namespace console { #endif } - int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) { + static int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) { #if defined(_WIN32) CONSOLE_SCREEN_BUFFER_INFO bufferInfo; if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) { @@ -303,7 +303,7 @@ namespace console { #endif } - void replace_last(char ch) { + static void replace_last(char ch) { #if defined(_WIN32) pop_cursor(); put_codepoint(&ch, 1, 1); @@ -312,7 +312,7 @@ namespace console { #endif } - void append_utf8(char32_t ch, std::string & out) { + static void append_utf8(char32_t ch, std::string & out) { if (ch <= 0x7F) { out.push_back(static_cast(ch)); } else if (ch <= 0x7FF) { @@ -333,7 +333,7 @@ namespace console { } // Helper function to remove the last UTF-8 character from a string - void pop_back_utf8_char(std::string & line) { + static void pop_back_utf8_char(std::string & line) { if (line.empty()) { return; } @@ -349,7 +349,7 @@ namespace console { line.erase(pos); } - bool readline_advanced(std::string & line, bool multiline_input) { + static bool readline_advanced(std::string & line, bool multiline_input) { if (out != stdout) { fflush(stdout); } @@ -452,7 +452,7 @@ namespace console { return has_more; } - bool readline_simple(std::string & line, bool multiline_input) { + static bool readline_simple(std::string & line, bool multiline_input) { #if defined(_WIN32) std::wstring wline; if (!std::getline(std::wcin, wline)) { diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index 177d1e3a8..5a545a807 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -9,7 +9,7 @@ namespace grammar_parser { // NOTE: assumes valid utf8 (but checks for overrun) // copied from llama.cpp - std::pair decode_utf8(const char * src) { + static std::pair decode_utf8(const char * src) { static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; uint8_t first_byte = static_cast(*src); uint8_t highbits = first_byte >> 4; @@ -24,19 +24,19 @@ namespace grammar_parser { return std::make_pair(value, pos); } - uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { + static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { uint32_t next_id = static_cast(state.symbol_ids.size()); auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); return result.first->second; } - uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { + static uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { uint32_t next_id = static_cast(state.symbol_ids.size()); state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id; return next_id; } - void add_rule( + static void add_rule( parse_state & state, uint32_t rule_id, const std::vector & rule) { @@ -46,11 +46,11 @@ namespace grammar_parser { state.rules[rule_id] = rule; } - bool is_word_char(char c) { + static bool is_word_char(char c) { return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); } - std::pair parse_hex(const char * src, int size) { + static std::pair parse_hex(const char * src, int size) { const char * pos = src; const char * end = src + size; uint32_t value = 0; @@ -73,7 +73,7 @@ namespace grammar_parser { return std::make_pair(value, pos); } - const char * parse_space(const char * src, bool newline_ok) { + static const char * parse_space(const char * src, bool newline_ok) { const char * pos = src; while (*pos == ' ' || *pos == '\t' || *pos == '#' || (newline_ok && (*pos == '\r' || *pos == '\n'))) { @@ -88,7 +88,7 @@ namespace grammar_parser { return pos; } - const char * parse_name(const char * src) { + static const char * parse_name(const char * src) { const char * pos = src; while (is_word_char(*pos)) { pos++; @@ -99,7 +99,7 @@ namespace grammar_parser { return pos; } - std::pair parse_char(const char * src) { + static std::pair parse_char(const char * src) { if (*src == '\\') { switch (src[1]) { case 'x': return parse_hex(src + 2, 2); @@ -129,7 +129,7 @@ namespace grammar_parser { uint32_t rule_id, bool is_nested); - const char * parse_sequence( + static const char * parse_sequence( parse_state & state, const char * src, const std::string & rule_name, @@ -247,7 +247,7 @@ namespace grammar_parser { return pos; } - const char * parse_rule(parse_state & state, const char * src) { + static const char * parse_rule(parse_state & state, const char * src) { const char * name_end = parse_name(src); const char * pos = parse_space(name_end, false); size_t name_len = name_end - src; @@ -285,7 +285,7 @@ namespace grammar_parser { } } - void print_grammar_char(FILE * file, uint32_t c) { + static void print_grammar_char(FILE * file, uint32_t c) { if (0x20 <= c && c <= 0x7f) { fprintf(file, "%c", static_cast(c)); } else { @@ -294,7 +294,7 @@ namespace grammar_parser { } } - bool is_char_element(llama_grammar_element elem) { + static bool is_char_element(llama_grammar_element elem) { switch (elem.type) { case LLAMA_GRETYPE_CHAR: return true; case LLAMA_GRETYPE_CHAR_NOT: return true; @@ -304,7 +304,7 @@ namespace grammar_parser { } } - void print_rule_binary(FILE * file, const std::vector & rule) { + static void print_rule_binary(FILE * file, const std::vector & rule) { for (auto elem : rule) { switch (elem.type) { case LLAMA_GRETYPE_END: fprintf(file, "END"); break; @@ -334,7 +334,7 @@ namespace grammar_parser { fprintf(file, "\n"); } - void print_rule( + static void print_rule( FILE * file, uint32_t rule_id, const std::vector & rule, diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index a99ece9a6..ed61125ea 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -9,12 +9,12 @@ #endif #ifdef LLAMA_DEFAULT_RMS_EPS -static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; +constexpr float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; #else -static const float rms_norm_eps = 5e-6f; +constexpr float rms_norm_eps = 5e-6f; #endif -float frand() { +static float frand() { return (float)rand()/(float)RAND_MAX; } @@ -25,19 +25,21 @@ struct random_normal_distribution { float max; }; -void init_random_normal_distribution(struct random_normal_distribution * rnd, int seed, float mean, float std, float min, float max) { +static void init_random_normal_distribution( + struct random_normal_distribution * rnd, int seed, float mean, float std, float min, float max +) { rnd->gen = std::mt19937(seed); rnd->nd = std::normal_distribution{mean, std}; rnd->min = min; rnd->max = max; } -float frand_normal(struct random_normal_distribution * rnd) { +static float frand_normal(struct random_normal_distribution * rnd) { const float r = rnd->nd(rnd->gen); return ((r < rnd->min) ? (rnd->min) : (r > rnd->max) ? (rnd->max) : r); } -void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { +static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { struct ggml_cplan plan = ggml_graph_plan(graph, n_threads); if (plan.work_size > 0) { @@ -48,13 +50,9 @@ void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, ggml_graph_compute(graph, &plan); } -struct ggml_tensor * randomize_tensor( - struct ggml_tensor * tensor, - int ndims, - const int64_t ne[], - float fmin, - float fmax) { - +static struct ggml_tensor * randomize_tensor( + struct ggml_tensor * tensor, int ndims, const int64_t ne[], float fmin, float fmax +) { switch (ndims) { case 1: for (int i0 = 0; i0 < ne[0]; i0++) { @@ -95,11 +93,9 @@ struct ggml_tensor * randomize_tensor( return tensor; } -struct ggml_tensor * randomize_tensor_normal( - struct ggml_tensor * tensor, - int ndims, - const int64_t ne[], - struct random_normal_distribution * rnd) { +static struct ggml_tensor * randomize_tensor_normal( + struct ggml_tensor * tensor, int ndims, const int64_t ne[], struct random_normal_distribution * rnd +) { float scale = 1.0; // xavier switch (ndims) { case 1: @@ -159,7 +155,7 @@ struct llama_hparams { } }; -uint32_t get_n_ff(const struct llama_hparams* hparams) { +static uint32_t get_n_ff(const struct llama_hparams* hparams) { const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult; return n_ff; } @@ -260,7 +256,7 @@ struct llama_model_lora { std::vector layers; }; -void init_model(struct llama_model * model) { +static void init_model(struct llama_model * model) { const auto & hparams = model->hparams; const uint32_t n_embd = hparams.n_embd; @@ -297,7 +293,7 @@ void init_model(struct llama_model * model) { } -void init_model_lora(struct llama_model_lora * model) { +static void init_model_lora(struct llama_model_lora * model) { const auto & hparams = model->hparams; const uint32_t n_embd = hparams.n_embd; @@ -340,7 +336,7 @@ void init_model_lora(struct llama_model_lora * model) { } } -void set_param_model(struct llama_model * model) { +static void set_param_model(struct llama_model * model) { const auto& hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; @@ -366,7 +362,7 @@ void set_param_model(struct llama_model * model) { } } -void set_param_model_lora(struct llama_model_lora * model) { +static void set_param_model_lora(struct llama_model_lora * model) { const auto& hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; @@ -397,7 +393,7 @@ void set_param_model_lora(struct llama_model_lora * model) { } } -void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) { +static void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) { const auto & hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; @@ -426,7 +422,9 @@ void randomize_model(struct llama_model * model, int seed, float mean, float std } -void randomize_model_lora(struct llama_model_lora * model, int seed, float mean, float std, float min, float max) { +static void randomize_model_lora( + struct llama_model_lora * model, int seed, float mean, float std, float min, float max +) { const auto & hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; @@ -459,7 +457,7 @@ void randomize_model_lora(struct llama_model_lora * model, int seed, float mean, } } -bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) { +static bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) { const auto & hparams = model->hparams; const uint32_t n_ctx = hparams.n_ctx; @@ -495,7 +493,7 @@ bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int return true; } -bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) { +static bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) { const auto & hparams = model->hparams; const uint32_t n_ctx = hparams.n_ctx; @@ -531,15 +529,15 @@ bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * return true; } -struct ggml_tensor * forward( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past) { - +static struct ggml_tensor * forward( + struct llama_model * model, + struct llama_kv_cache * cache, + struct ggml_context * ctx0, + struct ggml_cgraph * gf, + struct ggml_tensor * tokens_input, + const int n_tokens, + const int n_past +) { const int N = n_tokens; struct llama_kv_cache& kv_self = *cache; @@ -756,25 +754,25 @@ struct ggml_tensor * forward( return inpL; } -void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) { +static void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) { GGML_ASSERT(tensor->n_dims == 1); GGML_ASSERT(tensor->ne[0] == ne0); } -void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) { +static void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) { GGML_ASSERT(tensor->n_dims == 2); GGML_ASSERT(tensor->ne[0] == ne0); GGML_ASSERT(tensor->ne[1] == ne1); } -void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) { +static void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) { GGML_ASSERT(tensor->n_dims == 3); GGML_ASSERT(tensor->ne[0] == ne0); GGML_ASSERT(tensor->ne[1] == ne1); GGML_ASSERT(tensor->ne[2] == ne2); } -void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { +static void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { GGML_ASSERT(tensor->n_dims == 4); GGML_ASSERT(tensor->ne[0] == ne0); GGML_ASSERT(tensor->ne[1] == ne1); @@ -782,16 +780,16 @@ void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int6 GGML_ASSERT(tensor->ne[3] == ne3); } -struct ggml_tensor * forward_batch( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past, - const int n_batch) { - +static struct ggml_tensor * forward_batch( + struct llama_model * model, + struct llama_kv_cache * cache, + struct ggml_context * ctx0, + struct ggml_cgraph * gf, + struct ggml_tensor * tokens_input, + const int n_tokens, + const int n_past, + const int n_batch +) { const int N = n_tokens; struct llama_kv_cache& kv_self = *cache; @@ -1073,16 +1071,15 @@ struct ggml_tensor * forward_batch( return inpL; } - -struct ggml_tensor * forward_lora( - struct llama_model_lora * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past) { - +static struct ggml_tensor * forward_lora( + struct llama_model_lora * model, + struct llama_kv_cache * cache, + struct ggml_context * ctx0, + struct ggml_cgraph * gf, + struct ggml_tensor * tokens_input, + const int n_tokens, + const int n_past +) { const int N = n_tokens; struct llama_kv_cache& kv_self = *cache; @@ -1328,7 +1325,7 @@ struct ggml_tensor * forward_lora( return inpL; } -void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { +static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { assert(logits->n_dims == 2); assert(probs->n_dims == 2); assert(best_samples->n_dims == 1); @@ -1359,7 +1356,10 @@ void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, str } } -void sample_softmax_batch(struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { +static void sample_softmax_batch( + struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs, + struct ggml_tensor * best_samples +) { GGML_ASSERT(best_samples->n_dims == 2); GGML_ASSERT(logits->n_dims == 3); GGML_ASSERT(probs->n_dims == 3); @@ -1393,7 +1393,7 @@ void sample_softmax_batch(struct ggml_context * ctx, struct ggml_tensor * logits } } -void print_row(struct ggml_tensor * probs, int i) { +static void print_row(struct ggml_tensor * probs, int i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); printf(" %.2f", p); @@ -1401,7 +1401,7 @@ void print_row(struct ggml_tensor * probs, int i) { printf("\n"); } -void print_matrix(struct ggml_tensor * probs) { +static void print_matrix(struct ggml_tensor * probs) { assert(probs->n_dims == 2); for (int i = 0; i < probs->ne[1]; ++i) { for (int k = 0; k < probs->ne[0]; ++k) { @@ -1412,7 +1412,7 @@ void print_matrix(struct ggml_tensor * probs) { } } -void print_token(int token, int n_vocab) { +static void print_token(int token, int n_vocab) { for (int k = 0; k < token; ++k) { printf(" "); } @@ -1423,14 +1423,14 @@ void print_token(int token, int n_vocab) { printf("\n"); } -void print_tokens(struct ggml_tensor * tokens, int n_vocab) { +static void print_tokens(struct ggml_tensor * tokens, int n_vocab) { for (int i=0; ine[0]; ++i) { int token = ggml_get_i32_1d(tokens, i); print_token(token, n_vocab); } } -void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) { +static void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) { int n_tokens = tokens_input->ne[0]; int n_vocab = targets->ne[0]; float randomness = 0.0f; @@ -1451,7 +1451,9 @@ void get_example_targets(int example_id, struct ggml_tensor * tokens_input, stru } } -void get_example_targets_batch(struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) { +static void get_example_targets_batch( + struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets +) { GGML_ASSERT(tokens_input->n_dims == 2); GGML_ASSERT( targets->n_dims == 3); int n_tokens = tokens_input->ne[0]; @@ -1474,7 +1476,7 @@ void get_example_targets_batch(struct ggml_context * ctx, int example_id, struct } } -void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) { +static void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) { int n_tokens = tokens_input->ne[0]; int n_vocab = targets->ne[0]; for (int i=0; i response which is pointed to by callback_data. -void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { +static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { auto& callback_data = *static_cast(callback_data_ptr); // Mark beams as EOS as needed. for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 293b455d0..c291f0adf 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -115,7 +115,7 @@ struct TransformerWeights { } }; -void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { +static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { // we calloc instead of malloc to keep valgrind happy w->token_embedding_table = new float[p->vocab_size * p->dim](); printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); @@ -158,7 +158,7 @@ void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { } } -int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) { +static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) { if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1; if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1; if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; @@ -189,7 +189,7 @@ int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shar return 0; } -void print_sample_weights(TransformerWeights *w){ +static void print_sample_weights(TransformerWeights *w){ printf("----- Quick print of first of the weight vales of all the variables\n"); printf("%f\n", w->token_embedding_table[0]); printf("%f\n", w->rms_att_weight[0]); @@ -324,7 +324,7 @@ struct train_params { int mem_compute1_gb; }; -void print_params(struct my_llama_hparams * params) { +static void print_params(struct my_llama_hparams * params) { printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_embd: %d\n", __func__, params->n_embd); @@ -335,7 +335,7 @@ void print_params(struct my_llama_hparams * params) { printf("%s: n_rot: %d\n", __func__, params->n_rot); } -void init_model(struct my_llama_model * model) { +static void init_model(struct my_llama_model * model) { const auto & hparams = model->hparams; const uint32_t n_embd = hparams.n_embd; @@ -408,17 +408,17 @@ void init_model(struct my_llama_model * model) { } } -float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { +static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); return *ptr; } -int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { +static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); return *ptr; } -void print_row(struct ggml_tensor * probs, int i) { +static void print_row(struct ggml_tensor * probs, int i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = get_f32_2d(probs, k, i); printf(" %f", p); @@ -426,7 +426,7 @@ void print_row(struct ggml_tensor * probs, int i) { printf("\n"); } -void print_matrix(struct ggml_tensor * probs) { +static void print_matrix(struct ggml_tensor * probs) { assert(probs->n_dims == 2); for (int i = 0; i < probs->ne[1]; ++i) { for (int k = 0; k < probs->ne[0]; ++k) { @@ -531,7 +531,7 @@ struct llama_file { } }; -bool is_ggml_file(const char *filename) { +static bool is_ggml_file(const char * filename) { llama_file file(filename, "rb"); if (file.size < 4) { return false; @@ -540,7 +540,7 @@ bool is_ggml_file(const char *filename) { return magic == GGUF_MAGIC; } -static std::string llama_escape_whitespaces(const std::string& text) { +static std::string llama_escape_whitespaces(const std::string & text) { std::ostringstream out; for (char c : text) { if (c == ' ') out << "\xe2\x96\x81"; @@ -549,7 +549,7 @@ static std::string llama_escape_whitespaces(const std::string& text) { return out.str(); } -void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { +static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { if (is_ggml_file(filename)) { struct ggml_context * ctx_data = NULL; @@ -637,7 +637,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) } } -void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { +static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { int ct; switch (gg_weights->n_dims){ case 1: @@ -673,7 +673,9 @@ void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * kar } } -void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) { +static void save_as_llama_model( + struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename +) { // convert AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor @@ -785,7 +787,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod gguf_free(ctx); } -struct train_params get_default_train_params() { +static struct train_params get_default_train_params() { struct train_params params; params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; params.fn_llama2c_output_model = "ak_llama_model.bin"; @@ -835,7 +837,7 @@ struct train_params get_default_train_params() { return params; } -void print_usage(int /*argc*/, char ** argv, const struct train_params * params) { +static void print_usage(int /*argc*/, char ** argv, const struct train_params * params) { fprintf(stderr, "usage: %s [options]\n", argv[0]); fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); @@ -846,7 +848,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params) fprintf(stderr, "\n"); } -bool params_parse(int argc, char ** argv, struct train_params * params) { +static bool params_parse(int argc, char ** argv, struct train_params * params) { bool invalid_param = false; bool reqd_param_found = false; std::string arg; @@ -901,7 +903,7 @@ bool params_parse(int argc, char ** argv, struct train_params * params) { return true; } -std::string basename(const std::string &path) { +static std::string basename(const std::string &path) { size_t pos = path.find_last_of("/\\"); if (pos == std::string::npos) { return path; diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index a34010f10..9ab63a293 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -13,14 +13,14 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) -template +template static std::string to_string(const T & val) { std::stringstream ss; ss << val; return ss.str(); } -bool gguf_ex_write(const std::string & fname) { +static bool gguf_ex_write(const std::string & fname) { struct gguf_context * ctx = gguf_init_empty(); gguf_set_val_u8 (ctx, "some.parameter.uint8", 0x12); @@ -85,7 +85,7 @@ bool gguf_ex_write(const std::string & fname) { } // just read tensor info -bool gguf_ex_read_0(const std::string & fname) { +static bool gguf_ex_read_0(const std::string & fname) { struct gguf_init_params params = { /*.no_alloc = */ false, /*.ctx = */ NULL, @@ -143,7 +143,7 @@ bool gguf_ex_read_0(const std::string & fname) { } // read and create ggml_context containing the tensors and their data -bool gguf_ex_read_1(const std::string & fname) { +static bool gguf_ex_read_1(const std::string & fname) { struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { diff --git a/examples/main/main.cpp b/examples/main/main.cpp index a8179f1bf..e3cc3d39b 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -41,7 +41,8 @@ static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; static bool is_interacting = false; -void write_logfile( + +static void write_logfile( const llama_context * ctx, const gpt_params & params, const llama_model * model, const std::vector & input_tokens, const std::string & output, const std::vector & output_tokens @@ -86,7 +87,7 @@ void write_logfile( } #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) -void sigint_handler(int signo) { +static void sigint_handler(int signo) { if (signo == SIGINT) { if (!is_interacting) { is_interacting = true; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 3a1c8c28d..4620c43ac 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -28,9 +28,10 @@ struct results_log_softmax { float prob; }; -void write_logfile(const llama_context * ctx, const gpt_params & params, - const llama_model * model, const struct results_perplexity & results) { - +static void write_logfile( + const llama_context * ctx, const gpt_params & params, const llama_model * model, + const struct results_perplexity & results +) { if (params.logdir.empty()) { return; } @@ -76,7 +77,7 @@ void write_logfile(const llama_context * ctx, const gpt_params & params, fclose(logfile); } -std::vector softmax(const std::vector& logits) { +static std::vector softmax(const std::vector& logits) { std::vector probs(logits.size()); float max_logit = logits[0]; for (float v : logits) max_logit = std::max(max_logit, v); @@ -92,7 +93,7 @@ std::vector softmax(const std::vector& logits) { return probs; } -results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { +static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { float max_logit = logits[0]; for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]); double sum_exp = 0.0; @@ -100,9 +101,10 @@ results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp}; } -void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, - double & nll, double & nll2, float * logit_history, float * prob_history) { - +static void process_logits( + int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, + double & nll, double & nll2, float * logit_history, float * prob_history +) { std::mutex mutex; int counter = 0; auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { @@ -130,7 +132,7 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n } -results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { +static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` @@ -260,8 +262,7 @@ results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) return {tokens, std::exp(nll / count), logit_history, prob_history}; } -results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { - +static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { if (params.ppl_stride > 0) { return perplexity_v2(ctx, params); } @@ -400,8 +401,9 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { return {tokens, ppl, logit_history, prob_history}; } -std::vector hellaswag_evaluate_tokens(llama_context * ctx, const std::vector& tokens, int n_past, int n_batch, - int n_vocab, int n_thread) { +static std::vector hellaswag_evaluate_tokens( + llama_context * ctx, const std::vector& tokens, int n_past, int n_batch, int n_vocab, int n_thread +) { std::vector result; result.reserve(tokens.size() * n_vocab); size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch; @@ -421,7 +423,7 @@ std::vector hellaswag_evaluate_tokens(llama_context * ctx, const std::vec return result; } -void hellaswag_score(llama_context * ctx, const gpt_params & params) { +static void hellaswag_score(llama_context * ctx, const gpt_params & params) { // Calculates hellaswag score (acc_norm) from prompt // // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 6ce03ba7b..bfe70889a 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -34,8 +34,8 @@ struct quantize_stats_params { std::vector include_types; }; -const size_t HISTOGRAM_BUCKETS = 150; -const double HISTOGRAM_RANGE = 0.03; +constexpr size_t HISTOGRAM_BUCKETS = 150; +constexpr double HISTOGRAM_RANGE = 0.03; struct error_stats { size_t num_samples; @@ -44,8 +44,7 @@ struct error_stats { uint64_t error_histogram[HISTOGRAM_BUCKETS]; }; - -void quantize_stats_print_usage(int /*argc*/, char ** argv) { +static void quantize_stats_print_usage(int /*argc*/, char ** argv) { quantize_stats_params params; fprintf(stderr, "usage: %s [options]\n", argv[0]); fprintf(stderr, "\n"); @@ -71,7 +70,7 @@ 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) { +static 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; @@ -86,7 +85,7 @@ bool layer_included(const quantize_stats_params & params, const std::string & la } // Update error statistics given vectors with the before/after result of quantization -void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) { +static void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) { for (int64_t i = 0; i < nelements; i++) { double diff = input[i] - output[i]; stats.total_error += diff * diff; @@ -96,14 +95,14 @@ void update_error_stats(int64_t nelements, const float * input, const float * ou stats.num_samples += nelements; } -void combine_error_stats(error_stats & into, const error_stats & from) { +static void combine_error_stats(error_stats & into, const error_stats & from) { into.num_samples += from.num_samples; into.total_error += from.total_error; if (from.max_error > into.max_error) into.max_error = from.max_error; for (size_t i=0; inb[3] == tensor->nb[2]*tensor->ne[2]; } -void test_roundtrip_on_chunk( - const ggml_tensor * layer, - int64_t offset, - int64_t chunk_size, - const ggml_type_traits_t & qfns, - bool use_reference, - float * input_scratch, - char * quantized_scratch, - float * output_scratch, - error_stats & stats) { - +static void test_roundtrip_on_chunk( + const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits_t & qfns, bool use_reference, + float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats +) { if (layer->type == GGML_TYPE_F16) { for (int i = 0; i < chunk_size; i++) { input_scratch[i] = ggml_get_f32_1d(layer, i + offset); @@ -174,18 +166,11 @@ void test_roundtrip_on_chunk( // Run quantization function for a single layer and update error stats -void test_roundtrip_on_layer( - std::string & name, - bool print_layer_stats, - const ggml_type_traits_t & qfns, - bool use_reference, - const ggml_tensor * layer, - std::vector & input_scratch, - std::vector & quantized_scratch, - std::vector & output_scratch, - error_stats & total_error, - int max_thread = 0) { - +static void test_roundtrip_on_layer( + std::string & name, bool print_layer_stats, const ggml_type_traits_t & qfns, bool use_reference, + const ggml_tensor * layer, std::vector & input_scratch, std::vector & quantized_scratch, + std::vector & output_scratch, error_stats & total_error, int max_thread = 0 +) { assert(tensor_is_contiguous(layer)); error_stats layer_error {}; uint64_t nelements = ggml_nelements(layer); diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 1bf182482..300788c91 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -40,7 +40,7 @@ static const std::vector QUANT_OPTIONS = { }; -bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) { +static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) { std::string ftype_str; for (auto ch : ftype_str_in) { @@ -72,7 +72,7 @@ bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std: // usage: // ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] // -void usage(const char * executable) { +static void usage(const char * executable) { printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3f3c64650..1bb8e92c0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1083,8 +1083,9 @@ static json format_final_response(llama_server_context &llama, const std::string return res; } -static json format_partial_response(llama_server_context &llama, const std::string &content, const std::vector &probs) -{ +static json format_partial_response( + llama_server_context &llama, const std::string &content, const std::vector &probs +) { json res = json{ {"content", content}, {"stop", false}, @@ -1215,7 +1216,7 @@ static void log_server_request(const Request &req, const Response &res) }); } -bool is_at_eob(llama_server_context & server_context, const llama_token * tokens, const size_t n_tokens) { +static bool is_at_eob(llama_server_context &server_context, const llama_token *tokens, const size_t n_tokens) { return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); } @@ -1225,7 +1226,7 @@ bool is_at_eob(llama_server_context & server_context, const llama_token * tokens // * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. // This is also called when the stop condition is met. // Collect tokens into std::vector response which is pointed to by callback_data. -void beam_search_callback(void * callback_data, llama_beams_state beams_state) { +static void beam_search_callback(void *callback_data, llama_beams_state beams_state) { auto & llama = *static_cast(callback_data); // Mark beams as EOS as needed. for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { @@ -1258,7 +1259,8 @@ struct token_translator { 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) { +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama) +{ auto & gtps = llama.generated_token_probs; auto translator = token_translator{llama.ctx}; auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; diff --git a/llama.cpp b/llama.cpp index 8c5b74721..a65026122 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1,3 +1,4 @@ +#define LLAMA_API_INTERNAL #include "llama.h" #include "ggml.h" @@ -108,7 +109,7 @@ static size_t utf8_len(char src) { return lookup[highbits]; } -void replace_all(std::string & s, const std::string & search, const std::string & replace) { +static void replace_all(std::string & s, const std::string & search, const std::string & replace) { std::string result; for (size_t pos = 0; ; pos += search.length()) { auto new_pos = s.find(search, pos); @@ -1589,7 +1590,7 @@ struct llama_model_loader { // load LLaMA models // -std::string llama_model_ftype_name(enum llama_ftype ftype) { +static std::string llama_model_ftype_name(enum llama_ftype ftype) { if (ftype & LLAMA_FTYPE_GUESSED) { return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)"; } @@ -4295,7 +4296,7 @@ struct llama_grammar_candidate { // Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as // pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`. -std::pair, llama_partial_utf8> decode_utf8( +static std::pair, llama_partial_utf8> decode_utf8( const char * src, llama_partial_utf8 partial_start) { static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 }; @@ -5893,7 +5894,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } // TODO: after the GGUF PR, this likely won't work and needs to be updated -int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) { +static int llama_apply_lora_from_file_internal( + const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads +) { LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora); const int64_t t_start_lora_us = ggml_time_us(); @@ -6440,7 +6443,7 @@ struct llama_context * llama_new_context_with_model( return ctx; } -struct llama_context * llama_init_from_file( +static struct llama_context * llama_init_from_file( const char * path_model, struct llama_context_params params) { struct llama_model * model = llama_load_model_from_file(path_model, params); @@ -6645,7 +6648,7 @@ struct llama_data_file_context : llama_data_context { * llama_copy_state_data(ctx, &data_ctx); * */ -void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { +static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { // copy rng { std::stringstream rng_ss; @@ -7183,7 +7186,9 @@ void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) { } // For internal test use -const std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx) { +const std::vector> & llama_internal_get_tensor_map( + struct llama_context * ctx +) { return ctx->model.tensors_by_name; } diff --git a/llama.h b/llama.h index 37975bebe..c6ee038c7 100644 --- a/llama.h +++ b/llama.h @@ -540,7 +540,9 @@ extern "C" { struct ggml_tensor; -const std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx); +const std::vector> & llama_internal_get_tensor_map( + struct llama_context * ctx +); #endif // LLAMA_API_INTERNAL diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 48758cda8..e96372c4b 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -16,7 +16,7 @@ constexpr int kVecSize = 1 << 18; -float drawFromGaussianPdf(std::mt19937& rndm) { +static float drawFromGaussianPdf(std::mt19937& rndm) { constexpr double kScale = 1./(1. + std::mt19937::max()); constexpr double kTwoPiTimesScale = 6.28318530717958647692*kScale; static float lastX; @@ -28,7 +28,8 @@ float drawFromGaussianPdf(std::mt19937& rndm) { haveX = true; return r*cos(phi); } -void fillRandomGaussianFloats(std::vector& values, std::mt19937& rndm, float mean = 0) { + +static void fillRandomGaussianFloats(std::vector& values, std::mt19937& rndm, float mean = 0) { for (auto& v : values) v = mean + drawFromGaussianPdf(rndm); } diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index 8ab240202..ce4976858 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -36,15 +36,15 @@ #define GGML_PRINT(...) printf(__VA_ARGS__) -float frand(void) { +static float frand(void) { return (float)rand()/(float)RAND_MAX; } -int irand(int n) { +static int irand(int n) { return rand()%n; } -void get_random_dims(int64_t * dims, int ndims) { +static void get_random_dims(int64_t * dims, int ndims) { dims[0] = dims[1] = dims[2] = dims[3] = 1; for (int i = 0; i < ndims; i++) { @@ -52,7 +52,7 @@ void get_random_dims(int64_t * dims, int ndims) { } } -void get_random_dims_minmax(int64_t * dims, int ndims, int min, int max) { +static void get_random_dims_minmax(int64_t * dims, int ndims, int min, int max) { dims[0] = dims[1] = dims[2] = dims[3] = 1; for (int i = 0; i < ndims; i++) { @@ -61,12 +61,9 @@ void get_random_dims_minmax(int64_t * dims, int ndims, int min, int max) { } -struct ggml_tensor * get_random_tensor( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - float fmin, - float fmax) { +static struct ggml_tensor * get_random_tensor( + struct ggml_context * ctx0, int ndims, int64_t ne[], float fmin, float fmax +) { struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne); switch (ndims) { @@ -109,11 +106,11 @@ struct ggml_tensor * get_random_tensor( return result; } -float get_element(const struct ggml_tensor * t, int idx) { +static float get_element(const struct ggml_tensor * t, int idx) { return ((float *)t->data)[idx]; } -void set_element(struct ggml_tensor * t, int idx, float value) { +static void set_element(struct ggml_tensor * t, int idx, float value) { ((float *)t->data)[idx] = value; } diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 8d3c162d2..884af4054 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -13,24 +13,24 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f; -const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f; -const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f; -const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f; -const float MAX_DOT_PRODUCT_ERROR = 0.02f; +constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f; +constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f; +constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f; +constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f; +constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f; -const char* RESULT_STR[] = {"ok", "FAILED"}; +static const char* RESULT_STR[] = {"ok", "FAILED"}; // Generate synthetic data -void generate_data(float offset, size_t n, float * dst) { +static void generate_data(float offset, size_t n, float * dst) { for (size_t i = 0; i < n; i++) { dst[i] = 0.1 + 2*cosf(i + offset); } } // Calculate RMSE between two float arrays -float array_rmse(const float * a1, const float * a2, size_t n) { +static float array_rmse(const float * a1, const float * a2, size_t n) { double sum = 0; for (size_t i = 0; i < n; i++) { double diff = a1[i] - a2[i]; @@ -40,7 +40,7 @@ float array_rmse(const float * a1, const float * a2, size_t n) { } // Total quantization error on test data -float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { +static float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); @@ -50,7 +50,7 @@ float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, cons } // Total quantization error on test data -float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { +static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); std::vector tmp_out_ref(test_size); @@ -64,7 +64,7 @@ float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size); } -float dot_product(const float * a1, const float * a2, size_t test_size) { +static float dot_product(const float * a1, const float * a2, size_t test_size) { double sum = 0; for (size_t i = 0; i < test_size; i++) { sum += a1[i] * a2[i]; @@ -73,7 +73,9 @@ float dot_product(const float * a1, const float * a2, size_t test_size) { } // Total dot product error -float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) { +static float dot_product_error( + ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2 +) { std::vector tmp_q1(2*test_size); std::vector tmp_q2(2*test_size); diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index cbea7d452..01aa69877 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -61,22 +61,22 @@ inline int64_t cpu_cycles() { // Generate synthetic data -void generate_data(float offset, size_t n, float * dst) { +static void generate_data(float offset, size_t n, float * dst) { for (size_t i = 0; i < n; i++) { dst[i] = 0.1 + 2*cosf(i + offset); } } -float gigabytes_per_second(size_t bytes, int64_t usecs) { +static float gigabytes_per_second(size_t bytes, int64_t usecs) { return bytes / (float) usecs * 1000000 / (1024*1024*1024); } -void * align_with_offset(void * ptr, int offset) { +static void * align_with_offset(void * ptr, int offset) { size_t dummy_size = MAX_ALIGNMENT * 4; return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset; } -void benchmark_function(size_t size, size_t q_size, int64_t iterations, const std::function & function) { +static void benchmark_function(size_t size, size_t q_size, int64_t iterations, const std::function & function) { int64_t min_time_us = INT64_MAX; int64_t total_time_us = 0; int64_t min_time_cycles = INT64_MAX; @@ -108,7 +108,7 @@ void benchmark_function(size_t size, size_t q_size, int64_t iterations, const st printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * iterations, total_time_us)); } -void usage(char * argv[]) { +static void usage(char * argv[]) { printf("Benchmark quantization specific functions on synthetic data\n"); printf("\n"); printf("usage: %s [options]\n", argv[0]); diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 4437c3948..019c0d462 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -12,7 +12,8 @@ #include #include -void dump(const llama_token_data_array * candidates) { + +static void dump(const llama_token_data_array * candidates) { for (size_t i = 0; i < candidates->size; i++) { printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit); } @@ -21,9 +22,7 @@ void dump(const llama_token_data_array * candidates) { #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0) -void test_top_k(const std::vector & probs, - const std::vector & expected_probs, - int k) { +static void test_top_k(const std::vector & probs, const std::vector & expected_probs, int k) { size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); @@ -45,10 +44,7 @@ void test_top_k(const std::vector & probs, } -void test_top_p(const std::vector & probs, - const std::vector & expected_probs, - float p) { - +static void test_top_p(const std::vector & probs, const std::vector & expected_probs, float p) { size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); @@ -70,9 +66,7 @@ void test_top_p(const std::vector & probs, } -void test_tfs(const std::vector & probs, - const std::vector & expected_probs, - float z) { +static void test_tfs(const std::vector & probs, const std::vector & expected_probs, float z) { size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); @@ -93,9 +87,7 @@ void test_tfs(const std::vector & probs, } -void test_typical(const std::vector & probs, - const std::vector & expected_probs, - float p) { +static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); @@ -116,11 +108,10 @@ void test_typical(const std::vector & probs, } -void test_repetition_penalty( - const std::vector & probs, - const std::vector & last_tokens, - const std::vector & expected_probs, - float penalty) { +static void test_repetition_penalty( + const std::vector & probs, const std::vector & last_tokens, + const std::vector & expected_probs, float penalty +) { assert(probs.size() == expected_probs.size()); size_t n_vocab = probs.size(); @@ -145,11 +136,10 @@ void test_repetition_penalty( } -void test_frequency_presence_penalty( - const std::vector & probs, - const std::vector & last_tokens, - const std::vector & expected_probs, - float alpha_frequency, float alpha_presence) { +static void test_frequency_presence_penalty( + const std::vector & probs, const std::vector & last_tokens, + const std::vector & expected_probs, float alpha_frequency, float alpha_presence +) { assert(probs.size() == expected_probs.size()); size_t n_vocab = probs.size(); diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-llama.cpp index ab3d822f2..804ea2486 100644 --- a/tests/test-tokenizer-1-llama.cpp +++ b/tests/test-tokenizer-1-llama.cpp @@ -13,7 +13,7 @@ typedef int codepoint; -std::string codepoint_to_utf8(codepoint cp) { +static std::string codepoint_to_utf8(codepoint cp) { std::string result; if (0x00 <= cp && cp <= 0x7f) { result.push_back(cp);