forked from PABannier/bark.cpp
-
Notifications
You must be signed in to change notification settings - Fork 1
/
bark.cpp
2303 lines (1808 loc) · 74.6 KB
/
bark.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <random>
#include <regex>
#include <string>
#include "bark.h"
#include "encodec.h"
#define EPS_NORM 1e-5f
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MIN(a, b) ((a) < (b) ? (a) : (b))
static const size_t MB = 1024 * 1024;
class BarkProgressBar {
public:
BarkProgressBar(std::string func_name, double needed_progress) {
this->func_name = func_name;
this->needed_progress = needed_progress;
}
void update(double new_progress) {
current_progress += new_progress;
amount_of_filler = (int)((current_progress / needed_progress) * (double)pbar_length);
}
void print() {
printf("\r%s: %s", func_name.c_str(), initial_part.c_str());
for (int a = 0; a < amount_of_filler; a++) {
printf("%s", pbar_filler.c_str());
}
printf("%s", pbar_updater.c_str());
for (int b = 0; b < pbar_length - amount_of_filler; b++) {
printf(" ");
}
printf("%s (%d%%)", last_part.c_str(), (int)(100 * (current_progress / needed_progress)));
fflush(stdout);
}
std::string initial_part = "[", last_part = "]";
std::string pbar_filler = "=", pbar_updater = ">";
private:
std::string func_name;
double needed_progress, current_progress = 0;
int amount_of_filler, pbar_length = 50;
};
template <typename T>
static void read_safe(std::ifstream& fin, T& dest) {
fin.read((char*)&dest, sizeof(T));
}
template <typename T>
static void write_safe(std::ofstream& fout, T& dest) {
fout.write((char*)&dest, sizeof(T));
}
static void bark_print_statistics(gpt_model* model) {
printf("\n\n");
printf("%s: sample time = %8.2f ms / %lld tokens\n", __func__, model->t_sample_us / 1000.0f, model->n_sample);
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, model->t_predict_us / 1000.0f, model->t_predict_us / model->n_sample / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, model->t_main_us / 1000.0f);
printf("\n");
}
static void softmax(std::vector<float>& logits) {
// for numerical stability
float maxl = -INFINITY;
for (const auto& l : logits)
maxl = std::max(maxl, l);
// softmax
float sum = 0.0;
for (auto& l : logits) {
l = exp(l - maxl);
sum += l;
}
for (auto& l : logits)
l /= sum;
}
static bark_token gpt_multinomial_sample(
std::vector<float>& logits,
std::mt19937& rng,
float temp,
float* eos_p) {
int n_logits = logits.size();
for (int i = 0; i < n_logits; ++i)
logits[i] /= temp;
softmax(logits);
std::discrete_distribution<bark_token> dist(logits.begin(), logits.end());
int next = dist(rng);
// likelihood of EOS token
if (eos_p)
*eos_p = logits[logits.size() - 1];
return next;
}
static bark_token gpt_argmax_sample(std::vector<float>& logits, float* eos_p) {
int n_logits = logits.size();
// testing purposes
for (auto& l : logits) {
l /= 0.7f;
}
// likelihood of EOS token
softmax(logits);
if (eos_p)
*eos_p = logits[logits.size() - 1];
int next = 0;
float maxl = -INFINITY;
for (int i = 0; i < n_logits; i++) {
if (logits[i] > maxl) {
maxl = logits[i];
next = i;
}
}
return next;
}
static bark_token gpt_sample(
std::vector<float>& logits,
std::mt19937& rng,
float temp,
float* eos_p,
int64_t* t_sample_us,
int64_t* n_sample) {
int64_t t_sample_start_us = ggml_time_us();
bark_token res;
if (temp == 0.0f) {
res = gpt_argmax_sample(logits, eos_p);
} else {
res = gpt_multinomial_sample(logits, rng, temp, eos_p);
}
int64_t t_sample_end_us = ggml_time_us();
*t_sample_us += (t_sample_end_us - t_sample_start_us);
*n_sample += 1;
return res;
}
static bool ggml_quantize_weights(
std::ifstream& fin,
std::ofstream& fout,
const ggml_ftype ftype,
const std::vector<std::string>& to_quant,
const std::vector<std::string>& to_skip) {
ggml_type qtype = GGML_TYPE_F32;
switch (ftype) {
case GGML_FTYPE_MOSTLY_Q4_0:
qtype = GGML_TYPE_Q4_0;
break;
case GGML_FTYPE_MOSTLY_Q4_1:
qtype = GGML_TYPE_Q4_1;
break;
case GGML_FTYPE_MOSTLY_Q5_0:
qtype = GGML_TYPE_Q5_0;
break;
case GGML_FTYPE_MOSTLY_Q5_1:
qtype = GGML_TYPE_Q5_1;
break;
case GGML_FTYPE_MOSTLY_Q8_0:
qtype = GGML_TYPE_Q8_0;
break;
case GGML_FTYPE_UNKNOWN:
case GGML_FTYPE_ALL_F32:
case GGML_FTYPE_MOSTLY_F16:
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
case GGML_FTYPE_MOSTLY_Q2_K:
case GGML_FTYPE_MOSTLY_Q3_K:
case GGML_FTYPE_MOSTLY_Q4_K:
case GGML_FTYPE_MOSTLY_Q5_K:
case GGML_FTYPE_MOSTLY_Q6_K: {
fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
return false;
}
};
if (!ggml_is_quantized(qtype)) {
fprintf(stderr, "%s: invalid quantization type %d (%s)\n", __func__, qtype, ggml_type_name(qtype));
return false;
}
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<float> work;
std::vector<uint8_t> data_u8;
std::vector<ggml_fp16_t> data_f16;
std::vector<float> data_f32;
std::vector<int64_t> hist_all(1 << 4, 0);
int32_t n_tensors = 0;
read_safe(fin, n_tensors);
write_safe(fout, n_tensors);
for (int i = 0; i < n_tensors; i++) {
int32_t n_dims;
int32_t length;
int32_t ttype;
read_safe(fin, n_dims);
read_safe(fin, length);
read_safe(fin, ttype);
int32_t nelements = 1;
int32_t ne[4] = {1, 1, 1, 1};
for (int i = 0; i < n_dims; ++i) {
read_safe(fin, ne[i]);
nelements *= ne[i];
}
std::string name(length, 0);
fin.read(&name[0], length);
printf("%64s - [%5d, %5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type)ttype));
bool quantize = false;
// check if we should quantize this tensor
for (const auto& s : to_quant) {
if (std::regex_match(name, std::regex(s))) {
quantize = true;
break;
}
}
// check if we should skip this tensor
for (const auto& s : to_skip) {
if (std::regex_match(name, std::regex(s))) {
quantize = false;
break;
}
}
// quantize only 2D tensors
quantize &= (n_dims == 2);
if (quantize) {
if (ttype != GGML_TYPE_F32 && ttype != GGML_TYPE_F16) {
fprintf(stderr, "%s: unsupported ttype %d (%s) for integer quantization\n", __func__, ttype, ggml_type_name((ggml_type)ttype));
return false;
}
if (ttype == GGML_TYPE_F16) {
data_f16.resize(nelements);
fin.read(reinterpret_cast<char*>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
data_f32.resize(nelements);
for (int i = 0; i < nelements; ++i) {
data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
}
} else {
data_f32.resize(nelements);
fin.read(reinterpret_cast<char*>(data_f32.data()), nelements * sizeof(float));
}
ttype = qtype;
} else {
const int bpe = (ttype == 0) ? sizeof(float) : sizeof(uint16_t);
data_u8.resize(nelements * bpe);
fin.read(reinterpret_cast<char*>(data_u8.data()), nelements * bpe);
}
write_safe(fout, n_dims);
write_safe(fout, length);
write_safe(fout, ttype);
for (int i = 0; i < n_dims; ++i) {
write_safe(fout, ne[i]);
}
fout.write(&name[0], length);
if (quantize) {
work.resize(nelements); // for quantization
size_t cur_size = 0;
std::vector<int64_t> hist_cur(1 << 4, 0);
switch ((ggml_type)ttype) {
case GGML_TYPE_Q4_0: {
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q4_1: {
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_0: {
cur_size = ggml_quantize_q5_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_1: {
cur_size = ggml_quantize_q5_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q8_0: {
cur_size = ggml_quantize_q8_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
} break;
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_Q8_1:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_Q8_K:
case GGML_TYPE_COUNT: {
fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type)ttype));
return false;
}
}
fout.write(reinterpret_cast<char*>(work.data()), cur_size);
total_size_new += cur_size;
printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float) / 1024.0 / 1024.0, cur_size / 1024.0 / 1024.0);
for (int i = 0; i < (int)hist_cur.size(); ++i) {
hist_all[i] += hist_cur[i];
}
for (int i = 0; i < (int)hist_cur.size(); ++i) {
printf("%5.3f ", hist_cur[i] / (float)nelements);
}
printf("\n");
} else {
printf("size = %8.3f MB\n", data_u8.size() / 1024.0 / 1024.0);
fout.write(reinterpret_cast<char*>(data_u8.data()), data_u8.size());
total_size_new += data_u8.size();
}
total_size_org += nelements * sizeof(float);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
printf("%s: quant size = %8.2f MB | ftype = %d (%s)\n", __func__, total_size_new / 1024.0 / 1024.0, ftype, ggml_type_name(qtype));
{
int64_t sum_all = 0;
for (int i = 0; i < (int)hist_all.size(); ++i) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (int i = 0; i < (int)hist_all.size(); ++i) {
printf("%5.3f ", hist_all[i] / (float)sum_all);
}
printf("\n");
}
return true;
}
static size_t utf8_len(char src) {
const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4};
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
return lookup[highbits];
}
static std::string strip_accents(const std::string& in_str) {
std::string out_str;
std::map<std::string, char> accent_map = {
{"À", 'A'},
{"Á", 'A'},
{"Â", 'A'},
{"Ã", 'A'},
{"Ä", 'A'},
{"Å", 'A'},
{"à", 'a'},
{"á", 'a'},
{"â", 'a'},
{"ã", 'a'},
{"ä", 'a'},
{"å", 'a'},
{"È", 'E'},
{"É", 'E'},
{"Ê", 'E'},
{"Ë", 'E'},
{"è", 'e'},
{"é", 'e'},
{"ê", 'e'},
{"ë", 'e'},
{"Ì", 'I'},
{"Í", 'I'},
{"Î", 'I'},
{"Ï", 'I'},
{"ì", 'i'},
{"í", 'i'},
{"î", 'i'},
{"ï", 'i'},
{"Ò", 'O'},
{"Ó", 'O'},
{"Ô", 'O'},
{"Õ", 'O'},
{"Ö", 'O'},
{"ò", 'o'},
{"ó", 'o'},
{"ô", 'o'},
{"õ", 'o'},
{"ö", 'o'},
{"Ù", 'U'},
{"Ú", 'U'},
{"Û", 'U'},
{"Ü", 'U'},
{"ù", 'u'},
{"ú", 'u'},
{"û", 'u'},
{"ü", 'u'},
{"Ý", 'Y'},
{"ý", 'y'},
{"Ç", 'C'},
{"ç", 'c'},
{"Ñ", 'N'},
{"ñ", 'n'},
};
for (size_t i = 0; i < in_str.length();) {
int len = utf8_len(in_str[i]);
std::string cur = in_str.substr(i, len);
auto iter = accent_map.find(cur);
if (iter != accent_map.end())
out_str += iter->second;
else
out_str += cur;
i += len;
}
return out_str;
}
void bert_tokenize(
const bark_vocab* vocab,
const char* text,
int32_t* tokens,
int32_t* n_tokens,
int32_t n_max_tokens) {
std::string str = text;
std::vector<std::string> words;
int32_t t = 0;
auto* token_map = &vocab->token_to_id;
// split the text into words
{
str = strip_accents(text);
std::string pat = R"([[:punct:]]|[[:alpha:]]+|[[:digit:]]+)";
std::regex re(pat);
std::smatch m;
while (std::regex_search(str, m, re)) {
for (std::string x : m)
words.push_back(x);
str = m.suffix();
}
}
// apply wordpiece
for (const auto& word : words) {
if (word.size() == 0)
continue;
std::string prefix = "";
int i = 0;
int n = word.size();
loop:
while (i < n) {
if (t >= n_max_tokens - 1)
break;
int j = n;
while (j > i) {
auto it = token_map->find(prefix + word.substr(i, j - i));
if (it != token_map->end()) {
tokens[t++] = it->second;
i = j;
prefix = "##";
goto loop;
}
--j;
}
if (j == i) {
fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
prefix = "##";
++i;
}
}
}
*n_tokens = t;
}
static void bark_tokenize_input(struct bark_context* bctx, const std::string& text) {
auto& model = bctx->model.text_model;
bark_vocab* vocab = &bctx->model.vocab;
auto& params = bctx->params;
int32_t block_size = model.hparams.block_size;
int32_t max_ctx_size = std::min(block_size, 256);
int32_t n_tokens;
bark_sequence tokens(max_ctx_size);
bert_tokenize(vocab, text.data(), tokens.data(), &n_tokens, max_ctx_size);
for (int i = 0; i < (int)tokens.size(); i++)
tokens[i] += params.text_encoding_offset;
if (n_tokens < max_ctx_size) {
for (int i = n_tokens; i < max_ctx_size; i++)
tokens[i] = params.text_pad_token;
} else if (n_tokens > max_ctx_size) {
fprintf(stderr, "%s: input sequence is too long (%d > 256), truncating sequence", __func__, n_tokens);
}
tokens.resize(max_ctx_size);
// semantic history
for (int i = 0; i < 256; i++)
tokens.push_back(params.semantic_pad_token);
tokens.push_back(params.semantic_infer_token);
assert(tokens.size() == 256 + 256 + 1);
bctx->tokens = tokens;
printf("%s: prompt: '%s'\n", __func__, text.c_str());
printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, bctx->tokens.size());
for (int i = 0; i < std::min(8, (int)bctx->tokens.size()); i++) {
printf("%d ", bctx->tokens[i]);
}
printf("\n\n");
}
static bool bark_vocab_load(std::ifstream& fin, bark_vocab* vocab) {
int32_t n_vocab;
read_safe(fin, n_vocab);
std::string word;
std::vector<char> tmp;
tmp.reserve(128);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
read_safe(fin, len);
if (len > 0) {
tmp.resize(len);
fin.read(&tmp[0], tmp.size()); // read to buffer
word.assign(&tmp[0], tmp.size());
} else {
word = "";
}
vocab->token_to_id[word] = i;
vocab->id_to_token[i] = word;
}
return true;
}
static bool bark_model_load(std::ifstream& fin, gpt_model& model, int n_gpu_layers, bark_verbosity_level verbosity) {
// load hparams
{
auto& hparams = model.hparams;
read_safe(fin, hparams.n_layer);
read_safe(fin, hparams.n_head);
read_safe(fin, hparams.n_embd);
read_safe(fin, hparams.block_size);
read_safe(fin, hparams.bias);
read_safe(fin, hparams.n_in_vocab);
read_safe(fin, hparams.n_out_vocab);
read_safe(fin, hparams.n_lm_heads);
read_safe(fin, hparams.n_wtes);
read_safe(fin, hparams.ftype);
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
if (verbosity == bark_verbosity_level::MEDIUM || verbosity == bark_verbosity_level::HIGH) {
printf("%s: n_in_vocab = %d\n", __func__, hparams.n_in_vocab);
printf("%s: n_out_vocab = %d\n", __func__, hparams.n_out_vocab);
printf("%s: block_size = %d\n", __func__, hparams.block_size);
printf("%s: bias = %d\n", __func__, hparams.bias);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: n_lm_heads = %d\n", __func__, hparams.n_lm_heads);
printf("%s: n_wtes = %d\n", __func__, hparams.n_wtes);
printf("%s: ftype = %d\n", __func__, hparams.ftype);
printf("%s: qntvr = %d\n", __func__, qntvr);
}
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
}
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
// in order to save memory and also to speed up the computation
ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
if (wtype == GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid model file (bad ftype value %d)\n",
__func__, model.hparams.ftype);
return false;
}
auto& ctx = model.ctx;
size_t buffer_size = 0;
size_t n_tensors = 0;
// Evaluating context size
{
const auto& hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int block_size = hparams.block_size;
const int n_in_vocab = hparams.n_in_vocab;
const int n_out_vocab = hparams.n_out_vocab;
const int n_lm_heads = hparams.n_lm_heads;
const int n_wtes = hparams.n_wtes;
const int bias = hparams.bias;
buffer_size += n_embd * ggml_type_size(GGML_TYPE_F32); // ln_f_g
buffer_size += n_wtes * n_in_vocab * n_embd * ggml_type_size(wtype); // wtes
buffer_size += block_size * n_embd * ggml_type_size(GGML_TYPE_F32); // wpe
buffer_size += n_lm_heads * n_out_vocab * n_embd * ggml_type_size(wtype); // lm_head
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // ln_1_g
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // ln_2_g
buffer_size += n_layer * (3 * n_embd * n_embd * ggml_type_size(wtype)); // c_attn_attn_w
buffer_size += n_layer * (n_embd * n_embd * ggml_type_size(wtype)); // c_attn_proj_w
buffer_size += n_layer * (4 * n_embd * n_embd * ggml_type_size(wtype)); // c_mlp_fc_w
buffer_size += n_layer * (4 * n_embd * n_embd * ggml_type_size(wtype)); // c_mlp_proj_w
if (bias) {
buffer_size += n_embd * ggml_type_size(GGML_TYPE_F32); // ln_f_b
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // ln_1_b
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // ln_2_b
buffer_size += n_layer * (3 * n_embd * ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // c_attn_proj_b
buffer_size += n_layer * (4 * n_embd * ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
buffer_size += n_layer * (n_embd * ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
}
buffer_size += 10ull * MB; // object overhead
n_tensors = (1 + // ln_f_g
n_wtes + 1 + // wtes, wpe
2 * n_layer + // ln_1_g, ln_2_g
2 * n_layer + // c_attn_attn_w, c_attn_proj_w
2 * n_layer + // c_mlp_fc_w, c_mlp_proj_w
n_lm_heads + // lm_head
2 // memory_k, memory_v
);
if (bias) {
n_tensors += 1; // ln_f_b
n_tensors += 2 * n_layer; // ln_1_b, ln_2_b
n_tensors += 4 * n_layer; // c_attn_attn_b, c_attn_proj_b, c_mlp_fc_b, c_mlp_proj_b
}
if (verbosity == bark_verbosity_level::HIGH) {
printf("%s: ggml tensor size = %d bytes\n", __func__, (int)sizeof(ggml_tensor));
printf("%s: ggml ctx size = %6.2f MB\n", __func__, buffer_size / (1024.0 * 1024.0));
}
}
// create the ggml context
{
struct ggml_init_params params = {
/*.mem_size =*/ggml_tensor_overhead() * n_tensors,
/*.mem_buffer =*/NULL,
/*.no_alloc =*/true,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
}
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: using CUDA backend\n", __func__);
model.backend = ggml_backend_cuda_init();
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
}
#endif
#ifdef GGML_USE_METAL
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: using Metal backend\n", __func__);
ggml_metal_log_set_callback(ggml_log_callback_default, nullptr);
model.backend = ggml_backend_metal_init();
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
}
#endif
if (!model.backend) {
// fallback to CPU backend
if (verbosity == bark_verbosity_level::HIGH) {
fprintf(stderr, "%s: no backend specified, using CPU backend\n", __func__);
}
model.backend = ggml_backend_cpu_init();
}
if (!model.backend) {
if (verbosity == bark_verbosity_level::HIGH) {
fprintf(stderr, "%s: failed to initialize CPU backend\n", __func__);
}
return false;
}
// allocate weights buffer
model.buffer_w = ggml_backend_alloc_buffer(model.backend, buffer_size);
// prepare memory for the weights
{
const auto& hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int block_size = hparams.block_size;
const int n_in_vocab = hparams.n_in_vocab;
const int n_out_vocab = hparams.n_out_vocab;
const int n_lm_heads = hparams.n_lm_heads;
const int n_wtes = hparams.n_wtes;
const int bias = hparams.bias;
model.layers.resize(n_layer);
model.lm_heads.resize(n_lm_heads);
model.wtes.resize(n_wtes);
model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
if (bias) {
model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
}
model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, block_size);
for (int i = 0; i < n_wtes; i++) {
model.wtes[i] = ggml_new_tensor_2d(ctx, wtype, n_embd, n_in_vocab);
model.tensors["model/wte/" + std::to_string(i)] = model.wtes[i];
}
for (int i = 0; i < n_lm_heads; i++) {
model.lm_heads[i] = ggml_new_tensor_2d(ctx, wtype, n_embd, n_out_vocab);
model.tensors["model/lm_head/" + std::to_string(i)] = model.lm_heads[i];
}
model.tensors["model/ln_f/g"] = model.ln_f_g;
model.tensors["model/ln_f/b"] = model.ln_f_b;
model.tensors["model/wpe"] = model.wpe;
for (int i = 0; i < n_layer; ++i) {
auto& layer = model.layers[i];
layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
if (bias) {
layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3 * n_embd);
layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4 * n_embd);
layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
}
// map by name
model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
}
}
// key + value memory
{
const auto& hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int block_size = hparams.block_size;
const int n_lm_heads = hparams.n_lm_heads;
const int n_wtes = hparams.n_wtes;
const int n_mem = n_layer * block_size;
const int n_elements = n_embd * n_mem;
if (n_lm_heads == 1 && n_wtes == 1) {
// hack: if one LM head and one token embedding layer, we are loading weights
// of the text and coarse encoder. In this case, we need KV cache.
// for fine encoder, no need for KV cache, skip this part.
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
if (verbosity == bark_verbosity_level::HIGH) {
printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
}
// create a backend buffer (can be in host or device memory)
model.buffer_kv = ggml_backend_alloc_buffer(model.backend, memory_size + 256);
// allocate the tensors into the backend buffer
{
ggml_allocr* alloc = ggml_allocr_new_from_buffer(model.buffer_kv);
// this updates the pointers in the tensors to point to the correct location in the buffer
// this is necessary since the ggml_context is .no_alloc == true
// note that the buffer can actually be a device buffer, depending on the backend
ggml_allocr_alloc(alloc, model.memory_k);
ggml_allocr_alloc(alloc, model.memory_v);
ggml_allocr_free(alloc);
}
}
}
// load weights
{
ggml_allocr* alloc = ggml_allocr_new_from_buffer(model.buffer_w);
size_t total_size = 0;
std::vector<char> read_buf;
int32_t n_tensors;
read_safe(fin, n_tensors);
for (int i = 0; i < n_tensors; i++) {
int32_t n_dims;
int32_t length;
int32_t ttype;
read_safe(fin, n_dims);
read_safe(fin, length);
read_safe(fin, ttype);
int32_t nelements = 1;
int32_t ne[2] = {1, 1};
for (int i = 0; i < n_dims; ++i) {
read_safe(fin, ne[i]);
nelements *= ne[i];
}
std::string name(length, 0);
fin.read(&name[0], length);
if (model.tensors.find(name.data()) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
return false;
}
auto tensor = model.tensors[name];
ggml_set_name(tensor, name.c_str());
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
return false;
}
const size_t bpe = ggml_type_size(ggml_type(ttype));
if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements * bpe);
return false;
}
ggml_allocr_alloc(alloc, tensor);
if (ggml_backend_is_cpu(model.backend)
#ifdef GGML_USE_METAL
|| ggml_backend_is_metal(model.backend)
#endif
) {
// for the CPU and Metal backends, we can read directly into the device memory
fin.read(reinterpret_cast<char*>(tensor->data), ggml_nbytes(tensor));
} else {
// read into a temporary buffer first, then copy to device memory
read_buf.resize(ggml_nbytes(tensor));
fin.read(read_buf.data(), ggml_nbytes(tensor));
ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
}
if (verbosity == bark_verbosity_level::HIGH) {
printf("%48s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], "float", ggml_nbytes(tensor) / 1024.0 / 1024.0);
}
total_size += ggml_nbytes(tensor);
}
ggml_allocr_free(alloc);
if (verbosity == bark_verbosity_level::MEDIUM || verbosity == bark_verbosity_level::HIGH) {
printf("%s: model size = %8.2f MB\n", __func__, total_size / 1024.0 / 1024.0);
}
model.memsize = total_size;
}
return true;
}
static struct bark_model* bark_load_model_from_file(
const std::string& fname,
struct bark_model* model,
int n_gpu_layers,
bark_verbosity_level verbosity) {
if (verbosity == bark_verbosity_level::MEDIUM || verbosity == bark_verbosity_level::HIGH) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
}
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return nullptr;
}