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sampling.cpp 17 KB

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  1. #include "sampling.h"
  2. #include "common.h"
  3. #include <cmath>
  4. #include <unordered_map>
  5. // the ring buffer works similarly to std::deque, but with a fixed capacity
  6. // TODO: deduplicate with llama-impl.h
  7. template<typename T>
  8. struct ring_buffer {
  9. ring_buffer(size_t cap) : capacity(cap), data(cap) {}
  10. T & front() {
  11. if (sz == 0) {
  12. throw std::runtime_error("ring buffer is empty");
  13. }
  14. return data[first];
  15. }
  16. const T & front() const {
  17. if (sz == 0) {
  18. throw std::runtime_error("ring buffer is empty");
  19. }
  20. return data[first];
  21. }
  22. T & back() {
  23. if (sz == 0) {
  24. throw std::runtime_error("ring buffer is empty");
  25. }
  26. return data[pos];
  27. }
  28. const T & back() const {
  29. if (sz == 0) {
  30. throw std::runtime_error("ring buffer is empty");
  31. }
  32. return data[pos];
  33. }
  34. void push_back(const T & value) {
  35. if (sz == capacity) {
  36. // advance the start when buffer is full
  37. first = (first + 1) % capacity;
  38. } else {
  39. sz++;
  40. }
  41. data[pos] = value;
  42. pos = (pos + 1) % capacity;
  43. }
  44. T pop_front() {
  45. if (sz == 0) {
  46. throw std::runtime_error("ring buffer is empty");
  47. }
  48. T value = data[first];
  49. first = (first + 1) % capacity;
  50. sz--;
  51. return value;
  52. }
  53. const T & rat(size_t i) const {
  54. if (i >= sz) {
  55. throw std::runtime_error("ring buffer: index out of bounds");
  56. }
  57. return data[(first + sz - i - 1) % capacity];
  58. }
  59. std::vector<T> to_vector() const {
  60. std::vector<T> result;
  61. result.reserve(sz);
  62. for (size_t i = 0; i < sz; i++) {
  63. result.push_back(data[(first + i) % capacity]);
  64. }
  65. return result;
  66. }
  67. void clear() {
  68. // here only reset the status of the buffer
  69. sz = 0;
  70. first = 0;
  71. pos = 0;
  72. }
  73. bool empty() const {
  74. return sz == 0;
  75. }
  76. size_t size() const {
  77. return sz;
  78. }
  79. size_t capacity = 0;
  80. size_t sz = 0;
  81. size_t first = 0;
  82. size_t pos = 0;
  83. std::vector<T> data;
  84. };
  85. struct common_sampler {
  86. common_sampler_params params;
  87. struct llama_sampler * grmr;
  88. struct llama_sampler * chain;
  89. ring_buffer<llama_token> prev;
  90. std::vector<llama_token_data> cur;
  91. llama_token_data_array cur_p;
  92. void set_logits(struct llama_context * ctx, int idx) {
  93. const auto * logits = llama_get_logits_ith(ctx, idx);
  94. const int n_vocab = llama_n_vocab(llama_get_model(ctx));
  95. cur.resize(n_vocab);
  96. for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
  97. cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
  98. }
  99. cur_p = { cur.data(), cur.size(), -1, false };
  100. }
  101. };
  102. std::string common_sampler_params::print() const {
  103. char result[1024];
  104. snprintf(result, sizeof(result),
  105. "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
  106. "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
  107. "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
  108. "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
  109. penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
  110. dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
  111. top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
  112. mirostat, mirostat_eta, mirostat_tau);
  113. return std::string(result);
  114. }
  115. struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) {
  116. llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
  117. lparams.no_perf = params.no_perf;
  118. auto * result = new common_sampler {
  119. /* .params = */ params,
  120. /* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
  121. /* .chain = */ llama_sampler_chain_init(lparams),
  122. /* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
  123. /* .cur = */ {},
  124. /* .cur_p = */ {},
  125. };
  126. llama_sampler_chain_add(result->chain,
  127. llama_sampler_init_logit_bias(
  128. llama_n_vocab(model),
  129. params.logit_bias.size(),
  130. params.logit_bias.data()));
  131. llama_sampler_chain_add(result->chain,
  132. llama_sampler_init_penalties(
  133. llama_n_vocab (model),
  134. llama_token_eos(model),
  135. llama_token_nl (model),
  136. params.penalty_last_n,
  137. params.penalty_repeat,
  138. params.penalty_freq,
  139. params.penalty_present,
  140. params.penalize_nl,
  141. params.ignore_eos));
  142. if (params.mirostat == 0) {
  143. for (const auto & cnstr : params.samplers) {
  144. switch (cnstr) {
  145. case COMMON_SAMPLER_TYPE_DRY:
  146. {
  147. std::vector<const char*> c_breakers;
  148. c_breakers.reserve(params.dry_sequence_breakers.size());
  149. for (const auto& str : params.dry_sequence_breakers) {
  150. c_breakers.push_back(str.c_str());
  151. }
  152. llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
  153. }
  154. break;
  155. case COMMON_SAMPLER_TYPE_TOP_K:
  156. llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
  157. break;
  158. case COMMON_SAMPLER_TYPE_TOP_P:
  159. llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep));
  160. break;
  161. case COMMON_SAMPLER_TYPE_MIN_P:
  162. llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
  163. break;
  164. case COMMON_SAMPLER_TYPE_XTC:
  165. llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
  166. break;
  167. case COMMON_SAMPLER_TYPE_TFS_Z:
  168. llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
  169. break;
  170. case COMMON_SAMPLER_TYPE_TYPICAL_P:
  171. llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
  172. break;
  173. case COMMON_SAMPLER_TYPE_TEMPERATURE:
  174. llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
  175. break;
  176. case COMMON_SAMPLER_TYPE_INFILL:
  177. llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
  178. break;
  179. default:
  180. GGML_ASSERT(false && "unknown sampler type");
  181. }
  182. }
  183. llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
  184. } else if (params.mirostat == 1) {
  185. llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
  186. llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
  187. } else if (params.mirostat == 2) {
  188. llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
  189. llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
  190. } else {
  191. GGML_ASSERT(false && "unknown mirostat version");
  192. }
  193. return result;
  194. }
  195. void common_sampler_free(struct common_sampler * gsmpl) {
  196. if (gsmpl) {
  197. llama_sampler_free(gsmpl->grmr);
  198. llama_sampler_free(gsmpl->chain);
  199. delete gsmpl;
  200. }
  201. }
  202. void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
  203. if (accept_grammar) {
  204. llama_sampler_accept(gsmpl->grmr, token);
  205. }
  206. llama_sampler_accept(gsmpl->chain, token);
  207. gsmpl->prev.push_back(token);
  208. }
  209. void common_sampler_reset(struct common_sampler * gsmpl) {
  210. llama_sampler_reset(gsmpl->grmr);
  211. llama_sampler_reset(gsmpl->chain);
  212. }
  213. struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
  214. return new common_sampler {
  215. /* .params = */ gsmpl->params,
  216. /* .grmr = */ llama_sampler_clone(gsmpl->grmr),
  217. /* .chain = */ llama_sampler_clone(gsmpl->chain),
  218. /* .prev = */ gsmpl->prev,
  219. /* .cur = */ gsmpl->cur,
  220. /* .cur_p = */ gsmpl->cur_p,
  221. };
  222. }
  223. void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
  224. // TODO: measure grammar performance
  225. if (gsmpl) {
  226. llama_perf_sampler_print(gsmpl->chain);
  227. }
  228. if (ctx) {
  229. llama_perf_context_print(ctx);
  230. }
  231. }
  232. llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
  233. gsmpl->set_logits(ctx, idx);
  234. auto & grmr = gsmpl->grmr;
  235. auto & chain = gsmpl->chain;
  236. auto & cur_p = gsmpl->cur_p; // initialized by set_logits
  237. if (grammar_first) {
  238. llama_sampler_apply(grmr, &cur_p);
  239. }
  240. llama_sampler_apply(chain, &cur_p);
  241. GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
  242. const llama_token id = cur_p.data[cur_p.selected].id;
  243. if (grammar_first) {
  244. return id;
  245. }
  246. // check if it the sampled token fits the grammar
  247. {
  248. llama_token_data single_token_data = { id, 1.0f, 0.0f };
  249. llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
  250. llama_sampler_apply(grmr, &single_token_data_array);
  251. const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
  252. if (is_valid) {
  253. return id;
  254. }
  255. }
  256. // resampling:
  257. // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
  258. gsmpl->set_logits(ctx, idx);
  259. llama_sampler_apply(grmr, &cur_p);
  260. llama_sampler_apply(chain, &cur_p);
  261. GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
  262. return cur_p.data[cur_p.selected].id;
  263. }
  264. uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
  265. return llama_sampler_get_seed(gsmpl->chain);
  266. }
  267. // helpers
  268. llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
  269. return &gsmpl->cur_p;
  270. }
  271. llama_token common_sampler_last(const struct common_sampler * gsmpl) {
  272. return gsmpl->prev.rat(0);
  273. }
  274. std::string common_sampler_print(const struct common_sampler * gsmpl) {
  275. std::string result = "logits ";
  276. for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
  277. const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
  278. result += std::string("-> ") + llama_sampler_name(smpl) + " ";
  279. }
  280. return result;
  281. }
  282. std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) {
  283. n = std::min(n, (int) gsmpl->prev.size());
  284. if (n <= 0) {
  285. return "";
  286. }
  287. std::string result;
  288. result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab
  289. for (int i = n - 1; i >= 0; i--) {
  290. const llama_token id = gsmpl->prev.rat(i);
  291. GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
  292. result += common_token_to_piece(ctx_main, id);
  293. }
  294. return result;
  295. }
  296. char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
  297. switch (cnstr) {
  298. case COMMON_SAMPLER_TYPE_DRY: return 'd';
  299. case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
  300. case COMMON_SAMPLER_TYPE_TFS_Z: return 'f';
  301. case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
  302. case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
  303. case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
  304. case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
  305. case COMMON_SAMPLER_TYPE_XTC: return 'x';
  306. case COMMON_SAMPLER_TYPE_INFILL: return 'i';
  307. default : return '?';
  308. }
  309. }
  310. std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
  311. switch (cnstr) {
  312. case COMMON_SAMPLER_TYPE_DRY: return "dry";
  313. case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
  314. case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z";
  315. case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
  316. case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
  317. case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
  318. case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
  319. case COMMON_SAMPLER_TYPE_XTC: return "xtc";
  320. case COMMON_SAMPLER_TYPE_INFILL: return "infill";
  321. default : return "";
  322. }
  323. }
  324. std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
  325. std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
  326. { "dry", COMMON_SAMPLER_TYPE_DRY },
  327. { "top_k", COMMON_SAMPLER_TYPE_TOP_K },
  328. { "top_p", COMMON_SAMPLER_TYPE_TOP_P },
  329. { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
  330. { "min_p", COMMON_SAMPLER_TYPE_MIN_P },
  331. { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z },
  332. { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
  333. { "xtc", COMMON_SAMPLER_TYPE_XTC },
  334. { "infill", COMMON_SAMPLER_TYPE_INFILL },
  335. };
  336. // since samplers names are written multiple ways
  337. // make it ready for both system names and input names
  338. std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
  339. { "top-k", COMMON_SAMPLER_TYPE_TOP_K },
  340. { "top-p", COMMON_SAMPLER_TYPE_TOP_P },
  341. { "nucleus", COMMON_SAMPLER_TYPE_TOP_P },
  342. { "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
  343. { "typical", COMMON_SAMPLER_TYPE_TYPICAL_P },
  344. { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
  345. { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
  346. { "min-p", COMMON_SAMPLER_TYPE_MIN_P },
  347. { "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z },
  348. { "tfs", COMMON_SAMPLER_TYPE_TFS_Z },
  349. { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
  350. };
  351. std::vector<common_sampler_type> samplers;
  352. samplers.reserve(names.size());
  353. for (const auto & name : names) {
  354. auto sampler = sampler_canonical_name_map.find(name);
  355. if (sampler != sampler_canonical_name_map.end()) {
  356. samplers.push_back(sampler->second);
  357. } else {
  358. if (allow_alt_names) {
  359. sampler = sampler_alt_name_map.find(name);
  360. if (sampler != sampler_alt_name_map.end()) {
  361. samplers.push_back(sampler->second);
  362. }
  363. }
  364. }
  365. }
  366. return samplers;
  367. }
  368. std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
  369. std::unordered_map<char, common_sampler_type> sampler_name_map = {
  370. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY },
  371. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
  372. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z },
  373. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
  374. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
  375. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },
  376. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
  377. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
  378. { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
  379. };
  380. std::vector<common_sampler_type> samplers;
  381. samplers.reserve(chars.size());
  382. for (const auto & c : chars) {
  383. const auto sampler = sampler_name_map.find(c);
  384. if (sampler != sampler_name_map.end()) {
  385. samplers.push_back(sampler->second);
  386. }
  387. }
  388. return samplers;
  389. }