diff options
Diffstat (limited to 'common')
| -rw-r--r-- | common/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | common/common.cpp | 20 | ||||
| -rw-r--r-- | common/common.h | 28 | ||||
| -rw-r--r-- | common/ngram-cache.cpp | 280 | ||||
| -rw-r--r-- | common/ngram-cache.h | 94 |
5 files changed, 411 insertions, 13 deletions
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 10951693..1d840e5f 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -65,6 +65,8 @@ add_library(${TARGET} STATIC json.hpp train.h train.cpp + ngram-cache.h + ngram-cache.cpp ) if (BUILD_SHARED_LIBS) diff --git a/common/common.cpp b/common/common.cpp index de6eb960..69c2d5bf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -963,6 +963,22 @@ static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, } return true; } + if (arg == "-lcs" || arg == "--lookup-cache-static") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.lookup_cache_static = argv[i]; + return true; + } + if (arg == "-lcd" || arg == "--lookup-cache-dynamic") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.lookup_cache_dynamic = argv[i]; + return true; + } if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { if (++i >= argc) { invalid_param = true; @@ -1436,6 +1452,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" Hugging Face model file (default: unused)\n"); printf(" -ld LOGDIR, --logdir LOGDIR\n"); printf(" path under which to save YAML logs (no logging if unset)\n"); + printf(" -lcs FNAME, --lookup-cache-static FNAME\n"); + printf(" path to static lookup cache to use for lookup decoding (not updated by generation)\n"); + printf(" -lcd FNAME, --lookup-cache-dynamic FNAME\n"); + printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); diff --git a/common/common.h b/common/common.h index d827d4df..afa4cf6d 100644 --- a/common/common.h +++ b/common/common.h @@ -88,20 +88,22 @@ struct gpt_params { // // sampling parameters struct llama_sampling_params sparams; - std::string model = "models/7B/ggml-model-f16.gguf"; // model path - std::string model_draft = ""; // draft model for speculative decoding - std::string model_alias = "unknown"; // model alias - std::string model_url = ""; // model url to download - std::string hf_repo = ""; // HF repo - std::string hf_file = ""; // HF file - std::string prompt = ""; - std::string prompt_file = ""; // store the external prompt file name - std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state - std::string input_prefix = ""; // string to prefix user inputs with - std::string input_suffix = ""; // string to suffix user inputs with + std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding + std::string model_alias = "unknown"; // model alias + std::string model_url = ""; // model url to download + std::string hf_repo = ""; // HF repo + std::string hf_file = ""; // HF file + std::string prompt = ""; + std::string prompt_file = ""; // store the external prompt file name + std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state + std::string input_prefix = ""; // string to prefix user inputs with + std::string input_suffix = ""; // string to suffix user inputs with std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted - std::string logdir = ""; // directory in which to save YAML log files - std::string logits_file = ""; // file for saving *all* logits + std::string logdir = ""; // directory in which to save YAML log files + std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding + std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding + std::string logits_file = ""; // file for saving *all* logits std::vector<llama_model_kv_override> kv_overrides; diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp new file mode 100644 index 00000000..20703d30 --- /dev/null +++ b/common/ngram-cache.cpp @@ -0,0 +1,280 @@ +#include "ngram-cache.h" +#include "log.h" + +#include <fstream> + +void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, + std::vector<llama_token> & inp, int nnew, bool print_progress) { + const int64_t t_start_ms = ggml_time_ms(); + const int64_t inp_size = inp.size(); + + const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1); + int64_t n_done = 0; + + for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) { + const int64_t i_start = std::max(inp_size - nnew, ngram_size); + for (int64_t i = i_start; i < inp_size; ++i) { + const int64_t ngram_start = i - ngram_size; + llama_ngram ngram(&inp[ngram_start], ngram_size); + const llama_token token = inp[i]; + + llama_ngram_cache::iterator part_it = ngram_cache.find(ngram); + if (part_it == ngram_cache.end()) { + llama_ngram_cache_part part; + part.emplace(token, 1); + ngram_cache.emplace(ngram, part); + } else { + llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token); + if (token_count_it == part_it->second.end()) { + part_it->second.emplace(token, 1); + } else { + token_count_it->second++; + } + } + ++n_done; + + if (print_progress && n_done % 10000000 == 0) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s); + } + } + } +} + +// Helper function to get a token from the combined, speculative sequence of inp and draft. +static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) { + return i < inp.size() ? inp[i] : draft[1 + i - inp.size()]; +} + +// If sample size or percentage are below these thresholds the draft is aborted early: +constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1}; +constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50}; +constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2}; +constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; + +// Helper function that tries to draft a token from only the static ngram cache: +static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) { + llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + if (part_static_it == nc_static.end()) { + return -1; + } + const llama_ngram_cache_part part_static = part_static_it->second; + + int max_count_static = 0; + int sum_count_static = 0; + llama_token max_token = -1; + + for (std::pair<llama_token, int> token_count_static : part_static) { + const llama_token token = token_count_static.first; + const int32_t count_static = token_count_static.second; + + if (count_static > max_count_static) { + max_token = token; + max_count_static = count_static; + } + sum_count_static += count_static; + } + + if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { + return -1; + } + if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { + return -1; + } + return max_token; +} + +// Try to draft a token from primary cache (context/dynamic), validate with static cache: +static llama_token try_draft( + llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static, + const int * min_sample_size, const int * min_percent) { + + llama_token drafted_token = -1; + + for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) { + const llama_ngram ngram_primary = ngrams_primary[i]; + + llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); + if (part_primary_it == nc_primary.end()) { + continue; + } + const llama_ngram_cache_part part_primary = part_primary_it->second; + + int max_count_primary = 0; + int max_count_static = 0; + int sum_count_primary = 0; + llama_token max_token = -1; + + for (std::pair<llama_token, int> token_count_primary : part_primary) { + const llama_token token = token_count_primary.first; + + llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token); + + const int32_t count_primary = token_count_primary.second; + const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1; + + if (count_primary*count_static > max_count_primary*max_count_static) { + max_token = token; + max_count_primary = count_primary; + max_count_static = count_static; + } + sum_count_primary += count_primary; + } + + if (sum_count_primary < min_sample_size[i]) { + continue; + } + if (100*max_count_primary < min_percent[i]*sum_count_primary) { + continue;; + } + drafted_token = max_token; + } + + return drafted_token; +} + +void llama_ngram_cache_draft( + std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max, + llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static +) { + GGML_ASSERT(draft.size() == 1); + const int inp_size = inp.size(); + + if (inp_size < LLAMA_NGRAM_STATIC) { + return; + } + + while ((int) draft.size()-1 < n_draft) { + llama_token drafted_token = -1; + + const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; + llama_ngram ngram_static; + for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) { + ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j); + } + llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + llama_ngram_cache_part part_static; + if (part_static_it != nc_static.end()) { + part_static = part_static_it->second; + } + + // cd = context + dynamic + std::vector<llama_ngram> ngrams_cd; + for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) { + const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1; + llama_ngram ngram_cd; + for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) { + ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j); + } + ngrams_cd.push_back(ngram_cd); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); + } + if (drafted_token == -1) { + drafted_token = try_draft(nc_static, ngram_static); + } + + if (drafted_token == -1) { + break; + } + + LOG(" - draft candidate: token=%d\n", drafted_token); + draft.push_back(drafted_token); + } +} + +void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) { + std::ofstream file_out(filename, std::ios::binary); + for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) { + const llama_ngram ngram = item.first; + llama_ngram_cache_part token_counts = item.second; + GGML_ASSERT(!token_counts.empty()); + const int32_t ntokens = token_counts.size(); + GGML_ASSERT(ntokens > 0); + + file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(llama_ngram)); + file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t)); + for (std::pair<llama_token, int32_t> item2 : token_counts) { + const llama_token token = item2.first; + const int32_t count = item2.second; + GGML_ASSERT(count > 0); + + file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token)); + file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t)); + } + } + +} + +llama_ngram_cache llama_ngram_cache_load(std::string & filename) { + std::ifstream hashmap_file(filename, std::ios::binary); + if (!hashmap_file) { + throw std::ifstream::failure("Unable to open file " + filename); + } + llama_ngram_cache ngram_cache; + + llama_ngram ngram; + int32_t ntokens; + llama_token token; + int32_t count; + + char * ngramc = reinterpret_cast<char*>(&ngram); + char * ntokensc = reinterpret_cast<char*>(&ntokens); + char * tokenc = reinterpret_cast<char*>(&token); + char * countc = reinterpret_cast<char*>(&count); + while(hashmap_file.read(ngramc, sizeof(llama_ngram))) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t))); + GGML_ASSERT(ntokens > 0); + llama_ngram_cache_part token_counts; + + for (int i = 0; i < ntokens; ++i) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token))); + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t))); + GGML_ASSERT(count > 0); + token_counts.emplace(token, count); + } + + ngram_cache.emplace(ngram, token_counts); + } + GGML_ASSERT(hashmap_file.eof()); + + return ngram_cache; +} + +void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) { + for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) { + const llama_ngram ngram = ngram_part.first; + llama_ngram_cache_part part = ngram_part.second; + + llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram); + if (part_merged_it == ngram_cache_target.end()) { + ngram_cache_target.emplace(ngram, part); + continue; + } + + for (std::pair<llama_token, int32_t> token_count : part) { + const llama_token token = token_count.first; + const int32_t count = token_count.second; + GGML_ASSERT(count > 0); + + llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token); + if (token_count_merged_it == part_merged_it->second.end()) { + part_merged_it->second.emplace(token, count); + continue; + } + + token_count_merged_it->second += count; + } + } +} diff --git a/common/ngram-cache.h b/common/ngram-cache.h new file mode 100644 index 00000000..e4fa4cbd --- /dev/null +++ b/common/ngram-cache.h @@ -0,0 +1,94 @@ +#pragma once + +#include "llama.h" + +#include <unordered_map> +#include <string> +#include <vector> + +#define LLAMA_NGRAM_MIN 1 +#define LLAMA_NGRAM_MAX 4 +#define LLAMA_NGRAM_STATIC 2 + +// Data structures to map n-grams to empirical token probabilities: + +struct llama_ngram { + llama_token tokens[LLAMA_NGRAM_MAX]; + + llama_ngram() { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = -1; + } + } + + llama_ngram(const llama_token * input, const int ngram_size) { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = i < ngram_size ? input[i] : -1; + } + } + + bool operator==(const llama_ngram & other) const { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + if (tokens[i] != other.tokens[i]) { + return false; + } + } + return true; + } +}; + +struct llama_ngram_hash_function { + size_t operator()(const llama_ngram & ngram) const { + size_t hash = 0; + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + hash ^= std::hash<llama_token>{}(ngram.tokens[i]); + } + return hash; + } +}; + +// token -> number of times token has been seen +typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part; + +// n-gram -> empirical distribution of following tokens +typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache; + + +// Update an ngram cache with tokens. +// ngram_cache: the cache to modify. +// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data. +// inp_data: the token sequence with which to update ngram_cache. +// nnew: how many new tokens have been appended to inp_data since the last call to this function. +// print_progress: whether to print progress to stderr. +// +// In order to get correct results inp_data can ONLY BE APPENDED TO. +// Changes in the middle need a complete rebuild. +void llama_ngram_cache_update( + llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress); + +// Try to draft tokens from ngram caches. +// inp: the tokens generated so far. +// draft: the token sequence to draft. Expected to initially contain the previously sampled token. +// n_draft: maximum number of tokens to add to draft. +// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic. +// nc_context: ngram cache based on current context. +// nc_dynamic: ngram cache based on previous user generations. +// nc_static: ngram cache generated from a large text corpus, used for validation. +void llama_ngram_cache_draft( + std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max, + llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static); + +// Save an ngram cache to a file. +// ngram_cache: the ngram cache to save. +// filename: the path under which to save the ngram cache. +void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename); + +// Load an ngram cache saved with llama_ngram_cache_save. +// filename: the path from which to load the ngram cache. +// returns: an ngram cache containing the information saved to filename. +llama_ngram_cache llama_ngram_cache_load(std::string & filename); + +// Merge two ngram caches. +// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add. +// ngram_cache_add: the ngram cache to add to ngram_cache_target. +void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add); |