aditya/llama.cpp
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llama.cpp/utils.cpp
thement c9f670a177
Implement non-greedy tokenizer that tries to maximize token lengths (#242) 
* Implement non-greedy tokenizer that tries to maximize token lengths

* Insert single space in front of the prompt

- this is to match original llama tokenizer behavior

---------

Co-authored-by: Jakub Horak <jakub.horak@ibawizard.net>
2023-03-17 21:05:58 +01:00

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#include "utils.h"
#include <cassert>
#include <cstring>
#include <fstream>
#include <regex>
#include <iostream>
#include <iterator>
#include <string>
#include <math.h>
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__)
#include <alloca.h>
#endif
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
// determine sensible default number of threads.
// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.
#ifdef __linux__
std::ifstream cpuinfo("/proc/cpuinfo");
params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),
std::istream_iterator<std::string>(),
std::string("processor"));
#endif
if (params.n_threads == 0) {
params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
}
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg == "-s" || arg == "--seed") {
params.seed = std::stoi(argv[++i]);
} else if (arg == "-t" || arg == "--threads") {
params.n_threads = std::stoi(argv[++i]);
} else if (arg == "-p" || arg == "--prompt") {
params.prompt = argv[++i];
} else if (arg == "-f" || arg == "--file") {
std::ifstream file(argv[++i]);
std::copy(std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
back_inserter(params.prompt));
} else if (arg == "-n" || arg == "--n_predict") {
params.n_predict = std::stoi(argv[++i]);
} else if (arg == "--top_k") {
params.top_k = std::stoi(argv[++i]);
} else if (arg == "-c" || arg == "--ctx_size") {
params.n_ctx = std::stoi(argv[++i]);
} else if (arg == "--top_p") {
params.top_p = std::stof(argv[++i]);
} else if (arg == "--temp") {
params.temp = std::stof(argv[++i]);
} else if (arg == "--repeat_last_n") {
params.repeat_last_n = std::stoi(argv[++i]);
} else if (arg == "--repeat_penalty") {
params.repeat_penalty = std::stof(argv[++i]);
} else if (arg == "-b" || arg == "--batch_size") {
params.n_batch = std::stoi(argv[++i]);
} else if (arg == "-m" || arg == "--model") {
params.model = argv[++i];
} else if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
} else if (arg == "--interactive-start") {
params.interactive = true;
params.interactive_start = true;
} else if (arg == "--color") {
params.use_color = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
params.antiprompt = argv[++i];
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -i, --interactive run in interactive mode\n");
fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
fprintf(stderr, " prompt to start generation with (default: random)\n");
fprintf(stderr, " -f FNAME, --file FNAME\n");
fprintf(stderr, " prompt file to start generation.\n");
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n");
}
std::string gpt_random_prompt(std::mt19937 & rng) {
const int r = rng() % 10;
switch (r) {
case 0: return "So";
case 1: return "Once upon a time";
case 2: return "When";
case 3: return "The";
case 4: return "After";
case 5: return "If";
case 6: return "import";
case 7: return "He";
case 8: return "She";
case 9: return "They";
default: return "To";
}
return "The";
}
void replace(std::string & str, const std::string & needle, const std::string & replacement) {
size_t pos = 0;
while ((pos = str.find(needle, pos)) != std::string::npos) {
str.replace(pos, needle.length(), replacement);
pos += replacement.length();
}
}
std::map<std::string, int32_t> json_parse(const std::string & fname) {
std::map<std::string, int32_t> result;
// read file into string
std::string json;
{
std::ifstream ifs(fname);
if (!ifs) {
fprintf(stderr, "Failed to open %s\n", fname.c_str());
exit(1);
}
json = std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>()));
}
if (json[0] != '{') {
return result;
}
// parse json
{
bool has_key = false;
bool in_token = false;
std::string str_key = "";
std::string str_val = "";
int n = json.size();
for (int i = 1; i < n; ++i) {
if (!in_token) {
if (json[i] == ' ') continue;
if (json[i] == '"') {
in_token = true;
continue;
}
} else {
if (json[i] == '\\' && i+1 < n) {
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
++i;
} else if (json[i] == '"') {
if (has_key == false) {
has_key = true;
++i;
while (json[i] == ' ') ++i;
++i; // :
while (json[i] == ' ') ++i;
if (json[i] != '\"') {
while (json[i] != ',' && json[i] != '}') {
str_val += json[i++];
}
has_key = false;
} else {
in_token = true;
continue;
}
} else {
has_key = false;
}
::replace(str_key, "\\u0120", " " ); // \u0120 -> space
::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
::replace(str_key, "\\\"", "\""); // \\\" -> "
try {
result[str_key] = std::stoi(str_val);
} catch (...) {
//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
}
str_key = "";
str_val = "";
in_token = false;
continue;
}
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
}
}
}
return result;
}
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
std::vector<std::string> words;
// first split the text into words
{
std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
std::regex re(pat);
std::smatch m;
while (std::regex_search(str, m, re)) {
for (auto x : m) {
words.push_back(x);
}
str = m.suffix();
}
}
// find the longest tokens that form the words:
std::vector<gpt_vocab::id> tokens;
for (const auto & word : words) {
if (word.size() == 0) continue;
int i = 0;
int n = word.size();
while (i < n) {
int j = n;
while (j > i) {
auto it = vocab.token_to_id.find(word.substr(i, j-i));
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
break;
}
--j;
}
if (i == n) {
break;
}
if (j == i) {
auto sub = word.substr(i, 1);
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
tokens.push_back(vocab.token_to_id.at(sub));
} else {
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
}
++i;
}
}
}
return tokens;
}
// TODO: Calculate this constant from the vocabulary
#define MAX_TOKEN_LEN 18
// SentencePiece implementation after https://guillaume-be.github.io/2020-05-30/sentence_piece
std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
std::vector<gpt_vocab::id> res;
std::vector<int> score;
std::vector<gpt_vocab::id> prev;
int len = text.length();
score.resize(len + 1);
prev.resize(len + 1);
// Forward pass
for (int i = 0; i < len; i++) {
int max_len = std::min(len - i, MAX_TOKEN_LEN);
for (int sub_len = 1; sub_len <= len - i; sub_len++) {
auto sub = text.substr(i, sub_len);
auto token = vocab.token_to_id.find(sub);
if (token != vocab.token_to_id.end()) {
int token_score = sub.length() * sub.length();
int local_score = score[i] + token_score;
int next = i + sub_len;
if (score[next] < local_score) {
score[next] = local_score;
prev[next] = (*token).second;
}
}
}
}
// Backward pass
int i = len;
while (i > 0) {
gpt_vocab::id token_id = prev[i];
if (token_id == 0) {
// TODO: Return error or something more meaningful
printf("failed to tokenize string!\n");
break;
}
res.push_back(token_id);
auto token = (*vocab.id_to_token.find(token_id)).second;
i -= token.length();
}
if (bos) {
res.push_back(1); // TODO: replace with vocab.bos
}
// Pieces are in reverse order so correct that
std::reverse(res.begin(), res.end());
return res;
}
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
vocab.token_to_id = ::json_parse(fname);
for (const auto & kv : vocab.token_to_id) {
vocab.id_to_token[kv.second] = kv.first;
}
printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
// print the vocabulary
//for (auto kv : vocab.token_to_id) {
// printf("'%s' -> %d\n", kv.first.data(), kv.second);
//}
return true;
}
void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
return a.first > b.first;
});
logits_id.resize(top_k);
}
gpt_vocab::id llama_sample_top_p_top_k(
const gpt_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
double temp,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
const double scale = 1.0/temp;
for (int i = 0; i < n_logits; ++i) {
// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if (logits[i] < 0.0) {
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
} else {
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
}
} else {
logits_id.push_back(std::make_pair(logits[i]*scale, i));
}
}
}
sample_top_k(logits_id, top_k);
double maxl = -INFINITY;
for (const auto & kv : logits_id) {
maxl = std::max(maxl, kv.first);
}
// compute probs for the top K tokens
std::vector<double> probs;
probs.reserve(logits_id.size());
double sum = 0.0;
for (const auto & kv : logits_id) {
double p = exp(kv.first - maxl);
probs.push_back(p);
sum += p;
}
// normalize the probs
for (auto & p : probs) {
p /= sum;
}
if (top_p < 1.0f) {
double cumsum = 0.0f;
for (int i = 0; i < (int) probs.size(); i++) {
cumsum += probs[i];
if (cumsum >= top_p) {
probs.resize(i + 1);
logits_id.resize(i + 1);
break;
}
}
cumsum = 1.0/cumsum;
for (int i = 0; i < (int) probs.size(); i++) {
probs[i] *= cumsum;
}
}
//printf("\n");
//for (int i = 0; i < (int) 10; i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
//}
//printf("\n\n");
//exit(0);
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
return logits_id[idx].second;
}
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
amax = std::max(amax, fabsf(v));
}
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
pd += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0])*id;
const float v1 = (src[j + i*qk + l + 1])*id;
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
}
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
//printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
for (int i = 0; i < nb; i++) {
float min = std::numeric_limits<float>::max();
float max = std::numeric_limits<float>::min();
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
if (v < min) min = v;
if (v > max) max = v;
}
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
*(float *) pm = min;
pd += bs;
pm += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0] - min)*id;
const float v1 = (src[j + i*qk + l + 1] - min)*id;
const uint8_t vi0 = round(v0);
const uint8_t vi1 = round(v1);
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
}
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