diff options
Diffstat (limited to 'common/common.cpp')
| -rw-r--r-- | common/common.cpp | 140 |
1 files changed, 140 insertions, 0 deletions
diff --git a/common/common.cpp b/common/common.cpp index a1c3dc78..31382137 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_draft = std::stoi(argv[i]); } else if (arg == "--chunks") { if (++i >= argc) { invalid_param = true; @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.model = argv[i]; + } else if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_draft = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; @@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, " -md FNAME, --model-draft FNAME\n"); + fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str()); fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); fprintf(stdout, "\n"); @@ -832,6 +847,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to return result; } +// +// Sampling utils +// + +llama_token llama_sample_token( + struct llama_context * ctx, + struct llama_context * ctx_guidance, + struct llama_grammar * grammar, + const struct gpt_params & params, + const std::vector<llama_token> & last_tokens, + std::vector<llama_token_data> & candidates, + int idx) { + const int n_ctx = llama_n_ctx(ctx); + const int n_vocab = llama_n_vocab(ctx); + + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; + const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + + float * logits = llama_get_logits(ctx) + idx * n_vocab; + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + if (ctx_guidance) { + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + } + + // apply penalties + if (!last_tokens.empty()) { + const float nl_logit = logits[llama_token_nl(ctx)]; + const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + + llama_sample_repetition_penalty(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + if (grammar != NULL) { + llama_sample_grammar(ctx, &cur_p, grammar); + } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &cur_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + } + } + // printf("`%d`", candidates_p.size); + + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + + return id; +} + +// +// YAML utils +// + // returns true if successful, false otherwise bool create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -1070,6 +1209,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); |