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llama : style formatting + remove helper methods
1 parent 6c63550 commit afaba33

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3 files changed

+79
-115
lines changed

3 files changed

+79
-115
lines changed

‎ggml.h‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1744,12 +1744,12 @@ extern "C" {
17441744
GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);
17451745
GGML_API void * gguf_get_data (struct gguf_context * ctx);
17461746

1747-
GGML_API int gguf_get_n_kv(struct gguf_context * ctx);
1748-
GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);
1749-
GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
1747+
GGML_API int gguf_get_n_kv(struct gguf_context * ctx);
1748+
GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);
1749+
GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
1750+
17501751
GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);
1751-
GGML_API enum gguf_type gguf_get_arr_type (struct gguf_context * ctx, int i);
1752-
GGML_API void gguf_get_val (struct gguf_context * ctx, int i, void * val);
1752+
GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);
17531753

17541754
GGML_API const char * gguf_get_arr_str(struct gguf_context * ctx, int key_id, int i);
17551755
GGML_API float gguf_get_arr_f32(struct gguf_context * ctx, int key_id, int i);

‎gguf-llama.cpp‎

Lines changed: 67 additions & 105 deletions
Original file line numberDiff line numberDiff line change
@@ -510,22 +510,9 @@ struct llama_state {
510510
// global state
511511
static llama_state g_state;
512512

513-
template <typename T>
514-
static T checked_mul(T a, T b) {
515-
T ret = a * b;
516-
if (a != 0 && ret / a != b) {
517-
throw std::runtime_error(format("overflow multiplying %llu * %llu",
518-
(unsigned long long) a, (unsigned long long) b));
519-
}
520-
return ret;
521-
}
522-
523-
static size_t checked_div(size_t a, size_t b) {
524-
if (b == 0 || a % b != 0) {
525-
throw std::runtime_error(format("error dividing %zu / %zu", a, b));
526-
}
527-
return a / b;
528-
}
513+
//
514+
// model loading and saving
515+
//
529516

530517
static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
531518
char buf[256];
@@ -536,14 +523,6 @@ static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
536523
return buf;
537524
}
538525

539-
static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml_type type) {
540-
size_t size = ggml_type_size(type);
541-
for (uint32_t dim : ne) {
542-
size = checked_mul<size_t>(size, dim);
543-
}
544-
return size / ggml_blck_size(type);
545-
}
546-
547526
struct gguf_load_tensor {
548527
std::string name;
549528
enum ggml_type type = GGML_TYPE_F32;
@@ -573,20 +552,19 @@ struct gguf_file_loader {
573552

574553
struct ggml_context * ctx_data = NULL;
575554

576-
gguf_file_loader(const char * fname, gguf_load_tensors_map & tensors_map)
577-
: file(fname, "rb") {
555+
gguf_file_loader(const char * fname, gguf_load_tensors_map & tensors_map) : file(fname, "rb") {
578556
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
579557

580-
struct gguf_init_params params = {
581-
/*.no_alloc = */ true,
582-
/*.ctx = */ &ctx_data,
583-
};
558+
struct gguf_init_params params = {
559+
/*.no_alloc = */ true,
560+
/*.ctx = */ &ctx_data,
561+
};
584562

585-
gguf_ctx = gguf_init_from_file(fname, params);
586-
file_version = (enum gguf_file_version) gguf_get_version(gguf_ctx);
563+
gguf_ctx = gguf_init_from_file(fname, params);
564+
file_version = (enum gguf_file_version) gguf_get_version(gguf_ctx);
587565

588-
read_hparams();
589-
read_vocab();
566+
read_hparams();
567+
read_vocab();
590568
read_tensor_metadata(tensors_map);
591569
}
592570

@@ -636,18 +614,18 @@ struct gguf_file_loader {
636614

637615
void read_vocab() {
638616
vocab.id_to_token.resize(hparams.n_vocab);
639-
int token_idx = gguf_find_key(gguf_ctx, "tokenizer.ggml.tokens");
617+
618+
const int token_idx = gguf_find_key(gguf_ctx, "tokenizer.ggml.tokens");
640619
if (token_idx == -1) {
641620
throw std::runtime_error("cannot find token list in GGUF file\n");
642621
}
643622

644-
int score_idx = gguf_find_key(gguf_ctx, "tokenizer.ggml.scores");
623+
const int score_idx = gguf_find_key(gguf_ctx, "tokenizer.ggml.scores");
645624
if (score_idx == -1) {
646625
throw std::runtime_error("cannot find token scores list in GGUF file\n");
647626
}
648627

649628
for (uint32_t i = 0; i < hparams.n_vocab; i++) {
650-
651629
std::string word = gguf_get_arr_str(gguf_ctx, token_idx, i);
652630

653631
vocab.token_to_id[word] = i;
@@ -701,7 +679,7 @@ struct gguf_file_loader {
701679
tensor.file_off = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, i);
702680

703681
tensor.name = name;
704-
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
682+
tensor.size = ggml_nbytes(cur);
705683

706684
tensors_map.tensors.push_back(tensor);
707685
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
@@ -786,7 +764,7 @@ struct gguf_file_saver {
786764
gguf_type arr_type;
787765
int n_arr;
788766

789-
switch(vtype) {
767+
switch (vtype) {
790768
case GGUF_TYPE_BOOL:
791769
bool_val = gguf_get_val_bool(fl->gguf_ctx, i);
792770
file.write_val<bool>(key, GGUF_TYPE_BOOL, bool_val);
@@ -809,7 +787,7 @@ struct gguf_file_saver {
809787
break;
810788
case GGUF_TYPE_STRING:
811789
str_val = gguf_get_val_str(fl->gguf_ctx, i);
812-
file.write_val<std::string>(key, GGUF_TYPE_STRING, str_val);
790+
file.write_str(key, GGUF_TYPE_STRING, str_val);
813791
break;
814792
case GGUF_TYPE_UINT16:
815793
u16_val = gguf_get_val_u16(fl->gguf_ctx, i);
@@ -825,7 +803,7 @@ struct gguf_file_saver {
825803
break;
826804
case GGUF_TYPE_ARRAY:
827805
arr_type = gguf_get_arr_type(fl->gguf_ctx, i);
828-
n_arr = gguf_get_arr_n(fl->gguf_ctx, i);
806+
n_arr = gguf_get_arr_n (fl->gguf_ctx, i);
829807
if (arr_type == GGUF_TYPE_FLOAT32) {
830808
write_hparam_arr_f32(key, arr_type, i, n_arr);
831809
} else if (arr_type == GGUF_TYPE_STRING) {
@@ -922,20 +900,6 @@ struct llama_model_loader {
922900
}
923901
}
924902

925-
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
926-
auto it = tensors_map.name_to_idx.find(name);
927-
if (it == tensors_map.name_to_idx.end()) {
928-
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
929-
}
930-
gguf_load_tensor & lt = tensors_map.tensors.at(it->second);
931-
if (lt.ne != ne) {
932-
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
933-
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
934-
}
935-
936-
return get_tensor_for(lt, backend);
937-
}
938-
939903
struct ggml_tensor * get_tensor_for(gguf_load_tensor & lt, ggml_backend backend) {
940904
struct ggml_tensor * tensor;
941905
if (backend != GGML_BACKEND_CPU) {
@@ -959,16 +923,41 @@ struct llama_model_loader {
959923
return tensor;
960924
}
961925

926+
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
927+
auto it = tensors_map.name_to_idx.find(name);
928+
if (it == tensors_map.name_to_idx.end()) {
929+
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
930+
}
931+
gguf_load_tensor & lt = tensors_map.tensors.at(it->second);
932+
if (lt.ne != ne) {
933+
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
934+
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
935+
}
936+
937+
return get_tensor_for(lt, backend);
938+
}
939+
962940
void done_getting_tensors() const {
963941
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
964942
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
965943
}
966944
}
967945

968-
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, gguf_mlock * lmlock) {
969-
size_t data_size = 0;
946+
void load_data_for(gguf_load_tensor & lt) const {
947+
if (use_mmap) {
948+
lt.data = (uint8_t *) mapping->addr + lt.file_off;
949+
} else {
950+
gguf_file & file = file_loader->file;
951+
file.seek(lt.file_off, SEEK_SET);
952+
file.read_raw(lt.data, lt.size);
953+
}
954+
}
955+
956+
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, gguf_mlock * lmlock) {
957+
size_t data_size = 0;
970958
size_t prefetch_size = 0;
971-
size_t lock_size = 0;
959+
size_t lock_size = 0;
960+
972961
for (const gguf_load_tensor & lt : tensors_map.tensors) {
973962
data_size += lt.size;
974963
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
@@ -1030,31 +1019,6 @@ struct llama_model_loader {
10301019
done_size += lt.size;
10311020
}
10321021
}
1033-
1034-
void load_data_for(gguf_load_tensor & lt) {
1035-
if (use_mmap) {
1036-
lt.data = (uint8_t *) mapping->addr + lt.file_off;
1037-
} else {
1038-
gguf_file & file = file_loader->file;
1039-
file.seek(lt.file_off, SEEK_SET);
1040-
file.read_raw(lt.data, lt.size);
1041-
}
1042-
1043-
if (0) {
1044-
print_checksum(lt);
1045-
}
1046-
}
1047-
1048-
static void print_checksum(gguf_load_tensor & lt) {
1049-
uint32_t sum = 0;
1050-
for (size_t i = 0; i < lt.size; i++) {
1051-
uint8_t byte = lt.data[i];
1052-
sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash
1053-
}
1054-
fprintf(stderr, "%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
1055-
llama_format_tensor_shape(lt.ne).c_str(), lt.size);
1056-
}
1057-
10581022
};
10591023

10601024
//
@@ -1184,18 +1148,18 @@ int64_t llama_time_us() {
11841148
}
11851149

11861150
//
1187-
// model loading
1151+
// load LLaMA models
11881152
//
11891153

1190-
static const char *gguf_file_version_name(gguf_file_version version) {
1154+
static const char * gguf_file_version_name(gguf_file_version version) {
11911155
switch (version) {
11921156
case GGUF_FILE_VERSION_V1: return "GGUF V1 (latest)";
1193-
}
1157+
}
11941158

11951159
return "unknown";
11961160
}
11971161

1198-
static const char *llama_ftype_name(enum llama_ftype ftype) {
1162+
static const char * llama_ftype_name(enum llama_ftype ftype) {
11991163
switch (ftype) {
12001164
case LLAMA_FTYPE_ALL_F32: return "all F32";
12011165
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
@@ -1206,24 +1170,26 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
12061170
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
12071171
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
12081172
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
1173+
12091174
// K-quants
1210-
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
1175+
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
12111176
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
12121177
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
12131178
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
12141179
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
12151180
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
12161181
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
12171182
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
1218-
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
1219-
default: return "unknown, may not work";
1183+
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
1184+
1185+
default: return "unknown, may not work";
12201186
}
12211187
}
12221188

1223-
static const char *llama_model_type_name(e_model type) {
1189+
static const char * llama_model_type_name(e_model type) {
12241190
switch (type) {
1225-
case MODEL_3B: return "3B";
1226-
case MODEL_7B: return "7B";
1191+
case MODEL_3B: return "3B";
1192+
case MODEL_7B: return "7B";
12271193
case MODEL_13B: return "13B";
12281194
case MODEL_30B: return "30B";
12291195
case MODEL_65B: return "65B";
@@ -1604,7 +1570,6 @@ static struct ggml_cgraph * llama_build_graph(
16041570
const int64_t n_embd_head = hparams.n_embd_head();
16051571
const int64_t n_embd_gqa = hparams.n_embd_gqa();
16061572

1607-
16081573
GGML_ASSERT(n_embd_head == hparams.n_rot);
16091574

16101575
const float freq_base = hparams.rope_freq_base;
@@ -1713,7 +1678,7 @@ static struct ggml_cgraph * llama_build_graph(
17131678

17141679
struct ggml_tensor * inpSA = inpL;
17151680

1716-
lctx.use_buf(ctx0, 0);
1681+
llama_context::use_buf(ctx0, 0);
17171682

17181683
// norm
17191684
{
@@ -1852,7 +1817,7 @@ static struct ggml_cgraph * llama_build_graph(
18521817
ggml_set_name(cur, "result_wo");
18531818
}
18541819

1855-
lctx.use_buf(ctx0, 1);
1820+
llama_context::use_buf(ctx0, 1);
18561821

18571822
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
18581823
offload_func(inpFF);
@@ -1908,7 +1873,7 @@ static struct ggml_cgraph * llama_build_graph(
19081873
inpL = cur;
19091874
}
19101875

1911-
lctx.use_buf(ctx0, 0);
1876+
llama_context::use_buf(ctx0, 0);
19121877

19131878
// norm
19141879
{
@@ -1926,7 +1891,7 @@ static struct ggml_cgraph * llama_build_graph(
19261891
cur = ggml_mul_mat(ctx0, model.output, cur);
19271892
ggml_set_name(cur, "result_output");
19281893

1929-
lctx.use_buf(ctx0, -1);
1894+
llama_context::use_buf(ctx0, -1);
19301895

19311896
// logits -> probs
19321897
//cur = ggml_soft_max_inplace(ctx0, cur);
@@ -2996,9 +2961,8 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
29962961
}
29972962
}
29982963

2999-
const auto rejects =
3000-
llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
3001-
for (auto & reject : rejects) {
2964+
const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
2965+
for (const auto & reject : rejects) {
30022966
candidates->data[reject.index].logit = -INFINITY;
30032967
}
30042968

@@ -3725,7 +3689,7 @@ void llama_free(struct llama_context * ctx) {
37253689
int llama_model_quantize(
37263690
const char * fname_inp,
37273691
const char * fname_out,
3728-
const llama_model_quantize_params *params) {
3692+
const llama_model_quantize_params * params) {
37293693
try {
37303694
llama_model_quantize_internal(fname_inp, fname_out, params);
37313695
return 0;
@@ -4343,8 +4307,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
43434307
GGML_UNUSED(n_token_capacity);
43444308
GGML_UNUSED(n_token_count_out);
43454309

4346-
4347-
// TODO: implement with GGUF format
4310+
// TODO: implement with GGUF format
43484311
return true;
43494312
}
43504313

@@ -4389,7 +4352,6 @@ int llama_eval(
43894352
return 0;
43904353
}
43914354

4392-
43934355
int llama_eval_embd(
43944356
struct llama_context * ctx,
43954357
const float * embd,

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