359 lines
12 KiB
C++
359 lines
12 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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#define TH_VERBOSE // print token healing candidates
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struct token_healing_context {
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std::string prefix; // remaining prefix to generate (the input prompt's suffix)
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std::vector<std::string> vocab; // map token id to token piece
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// TODO consider using a prefix tree
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};
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static bool startswith(const std::string & str, const std::string & prefix) {
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return str.rfind(prefix, 0) != std::string::npos;
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}
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static bool token_healing_prefix_exists(const token_healing_context * th_ctx, const std::string & prefix) {
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for (const std::string & token : th_ctx->vocab) {
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if (startswith(token, prefix)) {
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return true;
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}
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}
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return false;
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}
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static std::vector<llama_token> token_healing_find_prefix(
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const token_healing_context * th_ctx,
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const std::string & prefix,
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const bool include_partial_prefix) {
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// Example: prefix=" world" -> " world", " worldwide", ...
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// If `include_partial_prefix`, include also: " w", " wo", ...
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std::vector<llama_token> candidates;
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const auto & vocab = th_ctx->vocab;
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for (size_t token_id = 0; token_id < vocab.size(); ++token_id) {
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if (startswith(vocab[token_id], prefix) ||
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(include_partial_prefix && startswith(prefix, vocab[token_id]))) {
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candidates.push_back((llama_token)token_id);
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}
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}
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return candidates;
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}
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static token_healing_context * token_healing_init(const llama_context * ctx) {
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auto * th_ctx = new token_healing_context;
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const llama_model * model = llama_get_model(ctx);
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const int32_t n_vocab = llama_n_vocab(model);
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std::vector<std::string> & vocab = th_ctx->vocab;
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vocab.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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vocab.emplace_back(llama_token_to_piece(ctx, token_id, true));
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}
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return th_ctx;
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}
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static void token_healing_free(token_healing_context * th_ctx) {
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delete th_ctx;
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}
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static int token_healing_heal(
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const llama_context * ctx,
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std::vector<llama_token> & tokens_list,
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const llama_token_healing_type th_type,
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token_healing_context * th_ctx,
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int n_rollback = 1) {
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if (tokens_list.empty()) {
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return 0;
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}
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const llama_model * model = llama_get_model(ctx);
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const bool is_dynamic = th_type == llama_token_healing_type::DYNAMIC_ONCE || th_type == llama_token_healing_type::DYNAMIC_MULTI;
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const int n_ctx = tokens_list.size();
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const int max_to_remove = is_dynamic ? n_ctx : std::min(n_rollback, n_ctx);
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int n_removed = 0;
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std::string prefix;
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// Roll back tokens a fixed amount or until there does not exist a token that can cover the prompt
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// and stop early if a special token is encountered
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while (n_removed < max_to_remove) {
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const llama_token next_token_id = tokens_list[n_ctx - n_removed - 1];
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if (llama_token_get_type(model, next_token_id) != LLAMA_TOKEN_TYPE_NORMAL) {
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// Don't roll back e.g. <|endoftext|> (if parse_special=true in llama_tokenize)
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break;
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}
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std::string new_prefix = th_ctx->vocab[next_token_id] + prefix;
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if (is_dynamic && !token_healing_prefix_exists(th_ctx, new_prefix)) {
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break;
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}
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n_removed += 1;
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prefix = new_prefix;
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}
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th_ctx->prefix = prefix;
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if (n_removed == 0) {
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return 0;
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}
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// If constrained decoding would give back the original prompt, there is no need to modify the context
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const bool is_multi_decoding = th_type == llama_token_healing_type::DYNAMIC_MULTI || th_type == llama_token_healing_type::ROLLBACK_MULTI;
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const std::vector<llama_token> candidates = token_healing_find_prefix(th_ctx, prefix, is_multi_decoding);
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fprintf(stderr, "token_healing: prefix = '%s' (%d tokens)\n", prefix.c_str(), n_removed);
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if (n_removed == 1 && candidates.size() == 1) {
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fprintf(stderr, "token_healing: nothing to heal\n");
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return 0;
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}
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#ifdef TH_VERBOSE
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if (!is_multi_decoding) {
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// Other healing types get printed during decoding
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for (const llama_token token_id : candidates) {
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fprintf(stderr, " [%6d] '%s'\n", token_id, th_ctx->vocab[token_id].c_str());
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}
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}
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#endif
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tokens_list.resize(n_ctx - n_removed);
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if (tokens_list.empty()) {
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// If the first token was removed, llama_decode would crash with an empty sequence, so add bos.
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tokens_list.emplace_back(llama_token_bos(model));
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}
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return n_removed;
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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if (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [PROMPT] [TOKEN_HEALING 0|1|d1|d|r[N]]\n" , argv[0]);
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return 1;
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}
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if (argc >= 2) {
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params.model = argv[1];
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}
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if (argc >= 3) {
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params.prompt = argv[2];
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}
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bool token_healing_enabled = true;
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auto th_type = llama_token_healing_type::DYNAMIC_MULTI;
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int th_n_rollback = 1;
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if (argc >= 4) {
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std::string value(argv[3]);
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/**/ if (value == "0" ) { token_healing_enabled = false; }
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else if (value == "1" ) { th_type = llama_token_healing_type::ROLLBACK_LAST; th_n_rollback = 1; }
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else if (value == "d1") { th_type = llama_token_healing_type::DYNAMIC_ONCE; }
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else if (value == "d" ) { th_type = llama_token_healing_type::DYNAMIC_MULTI; }
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else if (value[0] == 'r' ) {
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th_type = llama_token_healing_type::ROLLBACK_MULTI;
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th_n_rollback = std::stoi(value.substr(1));
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if (th_n_rollback <= 0) {
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token_healing_enabled = false;
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}
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} else {
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printf("usage: %s MODEL_PATH [PROMPT] [TOKEN_HEALING 0|1|d1|d|r[N]]\n" , argv[0]);
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return 1;
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}
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}
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if (params.prompt.empty()) {
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params.prompt = "Hello my name is";
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}
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// total length of the sequence including the prompt
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const int n_len = 32;
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// init LLM
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llama_backend_init();
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llama_numa_init(params.numa);
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// initialize the model
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llama_model_params model_params = llama_model_default_params();
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// model_params.n_gpu_layers = 99; // offload all layers to the GPU
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llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
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if (model == NULL) {
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fprintf(stderr , "%s: error: unable to load model\n" , __func__);
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return 1;
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}
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// initialize the context
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.n_ctx = 2048;
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ctx_params.n_threads = params.n_threads;
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ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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if (ctx == NULL) {
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fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
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return 1;
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}
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = ::llama_tokenize(ctx, params.prompt, true);
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token_healing_context * th_ctx = nullptr;
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if (token_healing_enabled) {
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th_ctx = token_healing_init(ctx);
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int th_n_tokens_removed = token_healing_heal(ctx, tokens_list, th_type, th_ctx, th_n_rollback);
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if (th_n_tokens_removed == 0) {
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token_healing_enabled = false;
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}
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}
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const int n_ctx = llama_n_ctx(ctx);
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
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LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
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return 1;
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}
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// print the prompt token-by-token
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fprintf(stderr, "\n");
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for (auto id : tokens_list) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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// create a llama_batch with size 512
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// we use this object to submit token data for decoding
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llama_batch batch = llama_batch_init(512, 0, 1);
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// evaluate the initial prompt
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for (size_t i = 0; i < tokens_list.size(); i++) {
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llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
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}
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// llama_decode will output logits only for the last token of the prompt
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batch.logits[batch.n_tokens - 1] = true;
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if (llama_decode(ctx, batch) != 0) {
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LOG_TEE("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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// main loop
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int n_cur = batch.n_tokens;
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int n_decode = 0;
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const auto t_main_start = ggml_time_us();
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while (n_cur <= n_len) {
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// sample the next token
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{
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auto n_vocab = llama_n_vocab(model);
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auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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if (token_healing_enabled) {
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// Constrain tokens based on the remaining token healing prefix
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// N.B. We could also set token constraints by setting rejected tokens' logits to -inf
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std::vector<llama_token> th_candidates;
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if (th_type == llama_token_healing_type::ROLLBACK_LAST || th_type == llama_token_healing_type::DYNAMIC_ONCE) {
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th_candidates = token_healing_find_prefix(th_ctx, th_ctx->prefix, false);
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} else {
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th_candidates = token_healing_find_prefix(th_ctx, th_ctx->prefix, true);
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#ifdef TH_VERBOSE
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fprintf(stderr, "\ntoken_healing: prefix = '%s'\n", th_ctx->prefix.c_str());
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for (const llama_token token_id : th_candidates) {
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fprintf(stderr, " [%6d] '%s'\n", token_id, th_ctx->vocab[token_id].c_str());
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}
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#endif
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}
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for (const llama_token token_id: th_candidates) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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} else {
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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// sample the most likely token
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const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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// is it an end of generation?
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
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LOG_TEE("\n");
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break;
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}
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std::string new_token_piece = llama_token_to_piece(ctx, new_token_id);
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LOG_TEE("%s", new_token_piece.c_str());
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fflush(stdout);
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if (token_healing_enabled) {
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if (new_token_piece.size() < th_ctx->prefix.size()) {
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// Shift prefix constraint (for multi step token healing)
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th_ctx->prefix = th_ctx->prefix.substr(new_token_piece.size());
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} else {
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th_ctx->prefix.clear();
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token_healing_enabled = false;
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}
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}
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// prepare the next batch
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llama_batch_clear(batch);
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// push this new token for next evaluation
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llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
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n_decode += 1;
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}
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n_cur += 1;
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch)) {
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fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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}
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LOG_TEE("\n");
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const auto t_main_end = ggml_time_us();
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LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
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__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
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llama_print_timings(ctx);
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fprintf(stderr, "\n");
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token_healing_free(th_ctx);
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llama_batch_free(batch);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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