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| // the ring buffer works similarly to std::deque, but with a fixed capacity | |
| // TODO: deduplicate with llama-impl.h | |
| template<typename T> | |
| struct ring_buffer { | |
| ring_buffer(size_t cap) : capacity(cap), data(cap) {} | |
| T & front() { | |
| if (sz == 0) { | |
| throw std::runtime_error("ring buffer is empty"); | |
| } | |
| return data[first]; | |
| } | |
| const T & front() const { | |
| if (sz == 0) { | |
| throw std::runtime_error("ring buffer is empty"); | |
| } | |
| return data[first]; | |
| } | |
| T & back() { | |
| if (sz == 0) { | |
| throw std::runtime_error("ring buffer is empty"); | |
| } | |
| return data[pos]; | |
| } | |
| const T & back() const { | |
| if (sz == 0) { | |
| throw std::runtime_error("ring buffer is empty"); | |
| } | |
| return data[pos]; | |
| } | |
| void push_back(const T & value) { | |
| if (sz == capacity) { | |
| // advance the start when buffer is full | |
| first = (first + 1) % capacity; | |
| } else { | |
| sz++; | |
| } | |
| data[pos] = value; | |
| pos = (pos + 1) % capacity; | |
| } | |
| T pop_front() { | |
| if (sz == 0) { | |
| throw std::runtime_error("ring buffer is empty"); | |
| } | |
| T value = data[first]; | |
| first = (first + 1) % capacity; | |
| sz--; | |
| return value; | |
| } | |
| const T & rat(size_t i) const { | |
| if (i >= sz) { | |
| throw std::runtime_error("ring buffer: index out of bounds"); | |
| } | |
| return data[(first + sz - i - 1) % capacity]; | |
| } | |
| std::vector<T> to_vector() const { | |
| std::vector<T> result; | |
| result.reserve(sz); | |
| for (size_t i = 0; i < sz; i++) { | |
| result.push_back(data[(first + i) % capacity]); | |
| } | |
| return result; | |
| } | |
| void clear() { | |
| // here only reset the status of the buffer | |
| sz = 0; | |
| first = 0; | |
| pos = 0; | |
| } | |
| bool empty() const { | |
| return sz == 0; | |
| } | |
| size_t size() const { | |
| return sz; | |
| } | |
| size_t capacity = 0; | |
| size_t sz = 0; | |
| size_t first = 0; | |
| size_t pos = 0; | |
| std::vector<T> data; | |
| }; | |
| struct common_sampler { | |
| common_sampler_params params; | |
| struct llama_sampler * grmr; | |
| struct llama_sampler * chain; | |
| ring_buffer<llama_token> prev; | |
| std::vector<llama_token_data> cur; | |
| llama_token_data_array cur_p; | |
| void set_logits(struct llama_context * ctx, int idx) { | |
| const auto * logits = llama_get_logits_ith(ctx, idx); | |
| const int n_vocab = llama_n_vocab(llama_get_model(ctx)); | |
| cur.resize(n_vocab); | |
| for (llama_token token_id = 0; token_id < n_vocab; token_id++) { | |
| cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; | |
| } | |
| cur_p = { cur.data(), cur.size(), -1, false }; | |
| } | |
| }; | |
| std::string common_sampler_params::print() const { | |
| char result[1024]; | |
| snprintf(result, sizeof(result), | |
| "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" | |
| "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" | |
| "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" | |
| "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", | |
| penalty_last_n, penalty_repeat, penalty_freq, penalty_present, | |
| dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, | |
| top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, | |
| mirostat, mirostat_eta, mirostat_tau); | |
| return std::string(result); | |
| } | |
| struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) { | |
| llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); | |
| lparams.no_perf = params.no_perf; | |
| auto * result = new common_sampler { | |
| /* .params = */ params, | |
| /* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"), | |
| /* .chain = */ llama_sampler_chain_init(lparams), | |
| /* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)), | |
| /* .cur = */ {}, | |
| /* .cur_p = */ {}, | |
| }; | |
| llama_sampler_chain_add(result->chain, | |
| llama_sampler_init_logit_bias( | |
| llama_n_vocab(model), | |
| params.logit_bias.size(), | |
| params.logit_bias.data())); | |
| llama_sampler_chain_add(result->chain, | |
| llama_sampler_init_penalties( | |
| llama_n_vocab (model), | |
| llama_token_eos(model), | |
| llama_token_nl (model), | |
| params.penalty_last_n, | |
| params.penalty_repeat, | |
| params.penalty_freq, | |
| params.penalty_present, | |
| params.penalize_nl, | |
| params.ignore_eos)); | |
| if (params.mirostat == 0) { | |
| for (const auto & cnstr : params.samplers) { | |
| switch (cnstr) { | |
| case COMMON_SAMPLER_TYPE_DRY: | |
| { | |
| std::vector<const char*> c_breakers; | |
| c_breakers.reserve(params.dry_sequence_breakers.size()); | |
| for (const auto& str : params.dry_sequence_breakers) { | |
| c_breakers.push_back(str.c_str()); | |
| } | |
| 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())); | |
| } | |
| break; | |
| case COMMON_SAMPLER_TYPE_TOP_K: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_TOP_P: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_MIN_P: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_XTC: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_TYPICAL_P: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_TEMPERATURE: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); | |
| break; | |
| case COMMON_SAMPLER_TYPE_INFILL: | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); | |
| break; | |
| default: | |
| GGML_ASSERT(false && "unknown sampler type"); | |
| } | |
| } | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); | |
| } else if (params.mirostat == 1) { | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); | |
| } else if (params.mirostat == 2) { | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); | |
| llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); | |
| } else { | |
| GGML_ASSERT(false && "unknown mirostat version"); | |
| } | |
| return result; | |
| } | |
| void common_sampler_free(struct common_sampler * gsmpl) { | |
| if (gsmpl) { | |
| llama_sampler_free(gsmpl->grmr); | |
| llama_sampler_free(gsmpl->chain); | |
| delete gsmpl; | |
| } | |
| } | |
| void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) { | |
| if (accept_grammar) { | |
| llama_sampler_accept(gsmpl->grmr, token); | |
| } | |
| llama_sampler_accept(gsmpl->chain, token); | |
| gsmpl->prev.push_back(token); | |
| } | |
| void common_sampler_reset(struct common_sampler * gsmpl) { | |
| llama_sampler_reset(gsmpl->grmr); | |
| llama_sampler_reset(gsmpl->chain); | |
| } | |
| struct common_sampler * common_sampler_clone(common_sampler * gsmpl) { | |
| return new common_sampler { | |
| /* .params = */ gsmpl->params, | |
| /* .grmr = */ llama_sampler_clone(gsmpl->grmr), | |
| /* .chain = */ llama_sampler_clone(gsmpl->chain), | |
| /* .prev = */ gsmpl->prev, | |
| /* .cur = */ gsmpl->cur, | |
| /* .cur_p = */ gsmpl->cur_p, | |
| }; | |
| } | |
| void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) { | |
| // TODO: measure grammar performance | |
| if (gsmpl) { | |
| llama_perf_sampler_print(gsmpl->chain); | |
| } | |
| if (ctx) { | |
| llama_perf_context_print(ctx); | |
| } | |
| } | |
| llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) { | |
| gsmpl->set_logits(ctx, idx); | |
| auto & grmr = gsmpl->grmr; | |
| auto & chain = gsmpl->chain; | |
| auto & cur_p = gsmpl->cur_p; // initialized by set_logits | |
| if (grammar_first) { | |
| llama_sampler_apply(grmr, &cur_p); | |
| } | |
| llama_sampler_apply(chain, &cur_p); | |
| GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration"); | |
| const llama_token id = cur_p.data[cur_p.selected].id; | |
| if (grammar_first) { | |
| return id; | |
| } | |
| // check if it the sampled token fits the grammar | |
| { | |
| llama_token_data single_token_data = { id, 1.0f, 0.0f }; | |
| llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false }; | |
| llama_sampler_apply(grmr, &single_token_data_array); | |
| const bool is_valid = single_token_data_array.data[0].logit != -INFINITY; | |
| if (is_valid) { | |
| return id; | |
| } | |
| } | |
| // resampling: | |
| // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain | |
| gsmpl->set_logits(ctx, idx); | |
| llama_sampler_apply(grmr, &cur_p); | |
| llama_sampler_apply(chain, &cur_p); | |
| GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration"); | |
| return cur_p.data[cur_p.selected].id; | |
| } | |
| uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) { | |
| return llama_sampler_get_seed(gsmpl->chain); | |
| } | |
| // helpers | |
| llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) { | |
| return &gsmpl->cur_p; | |
| } | |
| llama_token common_sampler_last(const struct common_sampler * gsmpl) { | |
| return gsmpl->prev.rat(0); | |
| } | |
| std::string common_sampler_print(const struct common_sampler * gsmpl) { | |
| std::string result = "logits "; | |
| for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) { | |
| const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); | |
| result += std::string("-> ") + llama_sampler_name(smpl) + " "; | |
| } | |
| return result; | |
| } | |
| std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) { | |
| n = std::min(n, (int) gsmpl->prev.size()); | |
| if (n <= 0) { | |
| return ""; | |
| } | |
| std::string result; | |
| result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab | |
| for (int i = n - 1; i >= 0; i--) { | |
| const llama_token id = gsmpl->prev.rat(i); | |
| GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen"); | |
| result += common_token_to_piece(ctx_main, id); | |
| } | |
| return result; | |
| } | |
| char common_sampler_type_to_chr(enum common_sampler_type cnstr) { | |
| switch (cnstr) { | |
| case COMMON_SAMPLER_TYPE_DRY: return 'd'; | |
| case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; | |
| case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; | |
| case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; | |
| case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; | |
| case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; | |
| case COMMON_SAMPLER_TYPE_XTC: return 'x'; | |
| case COMMON_SAMPLER_TYPE_INFILL: return 'i'; | |
| default : return '?'; | |
| } | |
| } | |
| std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { | |
| switch (cnstr) { | |
| case COMMON_SAMPLER_TYPE_DRY: return "dry"; | |
| case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; | |
| case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; | |
| case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; | |
| case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; | |
| case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; | |
| case COMMON_SAMPLER_TYPE_XTC: return "xtc"; | |
| case COMMON_SAMPLER_TYPE_INFILL: return "infill"; | |
| default : return ""; | |
| } | |
| } | |
| std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) { | |
| std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map { | |
| { "dry", COMMON_SAMPLER_TYPE_DRY }, | |
| { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, | |
| { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, | |
| { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, | |
| { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, | |
| { "xtc", COMMON_SAMPLER_TYPE_XTC }, | |
| { "infill", COMMON_SAMPLER_TYPE_INFILL }, | |
| }; | |
| // since samplers names are written multiple ways | |
| // make it ready for both system names and input names | |
| std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map { | |
| { "top-k", COMMON_SAMPLER_TYPE_TOP_K }, | |
| { "top-p", COMMON_SAMPLER_TYPE_TOP_P }, | |
| { "nucleus", COMMON_SAMPLER_TYPE_TOP_P }, | |
| { "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { "typical", COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, | |
| { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, | |
| }; | |
| std::vector<common_sampler_type> samplers; | |
| samplers.reserve(names.size()); | |
| for (const auto & name : names) { | |
| auto sampler = sampler_canonical_name_map.find(name); | |
| if (sampler != sampler_canonical_name_map.end()) { | |
| samplers.push_back(sampler->second); | |
| } else { | |
| if (allow_alt_names) { | |
| sampler = sampler_alt_name_map.find(name); | |
| if (sampler != sampler_alt_name_map.end()) { | |
| samplers.push_back(sampler->second); | |
| } | |
| } | |
| } | |
| } | |
| return samplers; | |
| } | |
| std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) { | |
| std::unordered_map<char, common_sampler_type> sampler_name_map = { | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, | |
| { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, | |
| }; | |
| std::vector<common_sampler_type> samplers; | |
| samplers.reserve(chars.size()); | |
| for (const auto & c : chars) { | |
| const auto sampler = sampler_name_map.find(c); | |
| if (sampler != sampler_name_map.end()) { | |
| samplers.push_back(sampler->second); | |
| } | |
| } | |
| return samplers; | |
| } | |