sampling.cpp 8.0 KB

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  1. #include "sampling.h"
  2. struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
  3. struct llama_sampling_context * result = new llama_sampling_context();
  4. result->params = params;
  5. result->grammar = nullptr;
  6. // if there is a grammar, parse it
  7. if (!params.grammar.empty()) {
  8. result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());
  9. // will be empty (default) if there are parse errors
  10. if (result->parsed_grammar.rules.empty()) {
  11. fprintf(stderr, "%s: failed to parse grammar\n", __func__);
  12. return nullptr;
  13. }
  14. std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
  15. result->grammar = llama_grammar_init(
  16. grammar_rules.data(),
  17. grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
  18. }
  19. result->prev.resize(params.n_prev);
  20. return result;
  21. }
  22. void llama_sampling_free(struct llama_sampling_context * ctx) {
  23. if (ctx->grammar != NULL) {
  24. llama_grammar_free(ctx->grammar);
  25. }
  26. delete ctx;
  27. }
  28. void llama_sampling_reset(llama_sampling_context * ctx) {
  29. if (ctx->grammar != NULL) {
  30. llama_grammar_free(ctx->grammar);
  31. ctx->grammar = NULL;
  32. }
  33. if (!ctx->parsed_grammar.rules.empty()) {
  34. std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
  35. ctx->grammar = llama_grammar_init(
  36. grammar_rules.data(),
  37. grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
  38. }
  39. std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
  40. ctx->cur.clear();
  41. }
  42. void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
  43. if (dst->grammar) {
  44. llama_grammar_free(dst->grammar);
  45. dst->grammar = nullptr;
  46. }
  47. if (src->grammar) {
  48. dst->grammar = llama_grammar_copy(src->grammar);
  49. }
  50. dst->prev = src->prev;
  51. }
  52. llama_token llama_sampling_last(llama_sampling_context * ctx) {
  53. return ctx->prev.back();
  54. }
  55. std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {
  56. const int size = ctx_sampling->prev.size();
  57. n = std::min(n, size);
  58. std::string result;
  59. for (int i = size - n; i < size; i++) {
  60. result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);
  61. }
  62. return result;
  63. }
  64. std::string llama_sampling_print(const llama_sampling_params & params) {
  65. char result[1024];
  66. snprintf(result, sizeof(result),
  67. "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
  68. "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
  69. "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
  70. params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
  71. params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
  72. params.mirostat, params.mirostat_eta, params.mirostat_tau);
  73. return std::string(result);
  74. }
  75. llama_token llama_sampling_sample(
  76. struct llama_sampling_context * ctx_sampling,
  77. struct llama_context * ctx_main,
  78. struct llama_context * ctx_cfg,
  79. const int idx) {
  80. const llama_sampling_params & params = ctx_sampling->params;
  81. const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
  82. const float temp = params.temp;
  83. const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
  84. const float top_p = params.top_p;
  85. const float min_p = params.min_p;
  86. const float tfs_z = params.tfs_z;
  87. const float typical_p = params.typical_p;
  88. const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
  89. const float penalty_repeat = params.penalty_repeat;
  90. const float penalty_freq = params.penalty_freq;
  91. const float penalty_present = params.penalty_present;
  92. const int mirostat = params.mirostat;
  93. const float mirostat_tau = params.mirostat_tau;
  94. const float mirostat_eta = params.mirostat_eta;
  95. const bool penalize_nl = params.penalize_nl;
  96. auto & prev = ctx_sampling->prev;
  97. auto & cur = ctx_sampling->cur;
  98. llama_token id = 0;
  99. float * logits = llama_get_logits_ith(ctx_main, idx);
  100. // apply params.logit_bias map
  101. for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
  102. logits[it->first] += it->second;
  103. }
  104. cur.clear();
  105. for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
  106. cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
  107. }
  108. llama_token_data_array cur_p = { cur.data(), cur.size(), false };
  109. if (ctx_cfg) {
  110. llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);
  111. }
  112. // apply penalties
  113. if (!prev.empty()) {
  114. const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
  115. llama_sample_repetition_penalties(ctx_main, &cur_p,
  116. prev.data() + prev.size() - penalty_last_n,
  117. penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
  118. if (!penalize_nl) {
  119. for (size_t idx = 0; idx < cur_p.size; idx++) {
  120. if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
  121. cur_p.data[idx].logit = nl_logit;
  122. break;
  123. }
  124. }
  125. }
  126. }
  127. if (ctx_sampling->grammar != NULL) {
  128. llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
  129. }
  130. if (temp < 0.0) {
  131. // greedy sampling, with probs
  132. llama_sample_softmax(ctx_main, &cur_p);
  133. id = cur_p.data[0].id;
  134. } else if (temp == 0.0) {
  135. // greedy sampling, no probs
  136. id = llama_sample_token_greedy(ctx_main, &cur_p);
  137. } else {
  138. if (mirostat == 1) {
  139. const int mirostat_m = 100;
  140. llama_sample_temp(ctx_main, &cur_p, temp);
  141. id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
  142. } else if (mirostat == 2) {
  143. llama_sample_temp(ctx_main, &cur_p, temp);
  144. id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
  145. } else {
  146. // temperature sampling
  147. size_t min_keep = std::max(1, params.n_probs);
  148. llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);
  149. llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);
  150. llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);
  151. llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);
  152. llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);
  153. llama_sample_temp (ctx_main, &cur_p, temp);
  154. id = llama_sample_token(ctx_main, &cur_p);
  155. //{
  156. // const int n_top = 10;
  157. // LOG("top %d candidates:\n", n_top);
  158. // for (int i = 0; i < n_top; i++) {
  159. // const llama_token id = cur_p.data[i].id;
  160. // (void)id; // To avoid a warning that id is unused when logging is disabled.
  161. // LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
  162. // }
  163. //}
  164. LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
  165. }
  166. }
  167. return id;
  168. }
  169. void llama_sampling_accept(
  170. struct llama_sampling_context * ctx_sampling,
  171. struct llama_context * ctx_main,
  172. llama_token id,
  173. bool apply_grammar) {
  174. ctx_sampling->prev.erase(ctx_sampling->prev.begin());
  175. ctx_sampling->prev.push_back(id);
  176. if (ctx_sampling->grammar != NULL && apply_grammar) {
  177. llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
  178. }
  179. }