mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-18 20:27:53 +00:00
ggml : sync latest ggml lib
This commit is contained in:
Georgi Gerganov
parent
7dfc11843c
commit
5feb0dffba
@ -52,6 +52,11 @@ bool ggml_common_quantize_0(
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case GGML_FTYPE_ALL_F32:
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case GGML_FTYPE_MOSTLY_F16:
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case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
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case GGML_FTYPE_MOSTLY_Q2_K:
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case GGML_FTYPE_MOSTLY_Q3_K:
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case GGML_FTYPE_MOSTLY_Q4_K:
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case GGML_FTYPE_MOSTLY_Q5_K:
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case GGML_FTYPE_MOSTLY_Q6_K:
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{
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fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
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return false;
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@ -187,6 +192,12 @@ bool ggml_common_quantize_0(
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_Q8_1:
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case GGML_TYPE_Q2_K:
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case GGML_TYPE_Q3_K:
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case GGML_TYPE_Q4_K:
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case GGML_TYPE_Q5_K:
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case GGML_TYPE_Q6_K:
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case GGML_TYPE_Q8_K:
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case GGML_TYPE_COUNT:
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{
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fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
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@ -6,13 +6,21 @@
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#include "dr_wav.h"
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#include <cmath>
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#include <cstring>
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#include <fstream>
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#include <regex>
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#include <locale>
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#include <codecvt>
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#include <sstream>
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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@ -52,7 +60,10 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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if (params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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} else {
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} else if (arg == "-tt" || arg == "--token_test") {
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params.token_test = argv[++i];
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}
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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@ -73,6 +84,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, " prompt to start generation with (default: random)\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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fprintf(stderr, " load prompt from a file\n");
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fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
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fprintf(stderr, " test tokenization\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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@ -117,6 +130,10 @@ std::string replace(const std::string & s, const std::string & from, const std::
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return result;
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}
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void gpt_vocab::add_special_token(const std::string & token) {
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special_tokens.push_back(token);
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}
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std::map<std::string, int32_t> json_parse(const std::string & fname) {
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std::map<std::string, int32_t> result;
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@ -208,8 +225,28 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) {
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return result;
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}
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void gpt_vocab::add_special_token(const std::string & token) {
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special_tokens.push_back(token);
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std::string convert_to_utf8(const std::wstring & input) {
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std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
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return converter.to_bytes(input);
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}
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std::wstring convert_to_wstring(const std::string & input) {
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std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
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return converter.from_bytes(input);
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}
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void gpt_split_words(std::string str, std::vector<std::string>& words) {
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const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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const std::regex re(pattern);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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}
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
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@ -218,63 +255,52 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
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// first split the text into words
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{
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std::string str = text;
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std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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// Generate the subpattern from the special_tokens vector if it's not empty
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if (!vocab.special_tokens.empty()) {
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const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
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std::string special_tokens_subpattern;
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for (const auto & token : vocab.special_tokens) {
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if (!special_tokens_subpattern.empty()) {
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special_tokens_subpattern += "|";
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}
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special_tokens_subpattern += token;
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special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
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}
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// Modify the regex pattern with the generated special tokens subpattern
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pat = special_tokens_subpattern + "|" + pat;
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}
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std::regex re(pat);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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for (auto x : m) {
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words.push_back(x);
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std::regex re(special_tokens_subpattern);
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std::smatch m;
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// Split the text by special tokens.
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while (std::regex_search(str, m, re)) {
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// Split the substrings in-between special tokens into words.
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gpt_split_words(m.prefix(), words);
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// Add matched special tokens as words.
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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str = m.suffix();
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// Remaining text without special tokens will be handled below.
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}
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gpt_split_words(str, words);
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}
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// find the longest tokens that form the words:
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// find the longest token that forms each word in words:
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std::vector<gpt_vocab::id> tokens;
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for (const auto & word : words) {
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if (word.size() == 0) continue;
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int i = 0;
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int n = word.size();
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while (i < n) {
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int j = n;
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while (j > i) {
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auto it = vocab.token_to_id.find(word.substr(i, j-i));
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if (it != vocab.token_to_id.end()) {
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for (int i = 0; i < (int) word.size(); ){
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for (int j = word.size() - 1; j >= i; j--){
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auto cand = word.substr(i, j-i+1);
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auto it = vocab.token_to_id.find(cand);
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if (it != vocab.token_to_id.end()){ // word.substr(i, j-i+1) in vocab
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tokens.push_back(it->second);
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i = j;
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j = n;
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i = j + 1;
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break;
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}
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--j;
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}
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if (i == n) {
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break;
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}
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if (j == i) {
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auto sub = word.substr(i, 1);
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if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
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tokens.push_back(vocab.token_to_id.at(sub));
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} else {
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
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else if (j == i){ // word.substr(i, 1) has no matching
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
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i++;
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}
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++i;
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}
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}
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}
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@ -282,6 +308,70 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
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return tokens;
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}
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std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) {
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std::vector<gpt_vocab::id> output;
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std::stringstream ss(input);
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std::string token;
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while (std::getline(ss, token, delimiter)) {
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output.push_back(std::stoi(token));
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}
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return output;
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}
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std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){
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if (fpath_test.empty()){
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fprintf(stderr, "%s : No test file found.\n", __func__);
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return std::map<std::string, std::vector<gpt_vocab::id>>();
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}
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std::map<std::string, std::vector<gpt_vocab::id>> tests;
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auto fin = std::ifstream(fpath_test, std::ios_base::in);
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const char * delimeter = " => ";
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const char del_tok = ',';
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std::string line;
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while (std::getline(fin, line)) {
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size_t delimiterPos = line.find(delimeter);
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if (delimiterPos != std::string::npos) {
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std::string text = line.substr(0, delimiterPos);
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std::string s_tokens = line.substr(delimiterPos + std::strlen(delimeter));
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tests[text] = parse_tokens_from_string(s_tokens, del_tok);
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}
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}
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return tests;
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}
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void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test){
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std::map<std::string, std::vector<gpt_vocab::id>> tests = extract_tests_from_file(fpath_test);
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size_t n_fails = 0;
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for (const auto & test : tests) {
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std::vector<gpt_vocab::id> tokens = gpt_tokenize(vocab, test.first);
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if (tokens != test.second){
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n_fails++;
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// print out failure cases
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fprintf(stderr, "%s : failed test: '%s'\n", __func__, test.first.c_str());
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fprintf(stderr, "%s : tokens in hf: ", __func__);
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for (const auto & t : test.second) {
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fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
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}
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fprintf(stderr, "\n");
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fprintf(stderr, "%s : tokens in ggml: ", __func__);
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for (const auto & t : tokens) {
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fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
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}
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fprintf(stderr, "\n");
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}
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}
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fprintf(stderr, "%s : %zu tests failed out of %zu tests.\n", __func__, n_fails, tests.size());
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}
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
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printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
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@ -381,6 +471,122 @@ gpt_vocab::id gpt_sample_top_k_top_p(
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return logits_id[idx].second;
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}
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gpt_vocab::id gpt_sample_top_k_top_p_repeat(
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const gpt_vocab & vocab,
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const float * logits,
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const int32_t * last_n_tokens_data,
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size_t last_n_tokens_data_size,
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int top_k,
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double top_p,
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double temp,
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int repeat_last_n,
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float repeat_penalty,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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const auto * plogits = logits;
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const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
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if (temp <= 0) {
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// select the token with the highest logit directly
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float max_logit = plogits[0];
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gpt_vocab::id max_id = 0;
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for (int i = 1; i < n_logits; ++i) {
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if (plogits[i] > max_logit) {
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max_logit = plogits[i];
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max_id = i;
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}
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}
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return max_id;
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}
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const float scale = 1.0f/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (plogits[i] < 0.0f) {
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logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
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}
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} else {
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logits_id.push_back(std::make_pair(plogits[i]*scale, i));
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}
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}
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}
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < top_k; i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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top_k = i + 1;
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probs.resize(top_k);
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logits_id.resize(top_k);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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// printf("\n");
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// for (int i = 0; i < (int) probs.size(); i++) {
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// for (int i = 0; i < 10; i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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// }
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
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drwav wav;
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std::vector<uint8_t> wav_data; // used for pipe input from stdin
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@ -26,8 +26,9 @@ struct gpt_params {
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int32_t n_batch = 8; // batch size for prompt processing
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std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
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std::string prompt;
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std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
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std::string prompt = "";
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std::string token_test = "";
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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@ -61,6 +62,12 @@ struct gpt_vocab {
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// poor-man's JSON parsing
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std::map<std::string, int32_t> json_parse(const std::string & fname);
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std::string convert_to_utf8(const std::wstring & input);
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std::wstring convert_to_wstring(const std::string & input);
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void gpt_split_words(std::string str, std::vector<std::string>& words);
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// split text into tokens
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//
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// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
|
||||
@ -73,6 +80,15 @@ std::map<std::string, int32_t> json_parse(const std::string & fname);
|
||||
//
|
||||
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
|
||||
|
||||
// test outputs of gpt_tokenize
|
||||
//
|
||||
// - compare with tokens generated by the huggingface tokenizer
|
||||
// - test cases are chosen based on the model's main language (under 'prompt' directory)
|
||||
// - if all sentences are tokenized identically, print 'All tests passed.'
|
||||
// - otherwise, print sentence, huggingface tokens, ggml tokens
|
||||
//
|
||||
void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test);
|
||||
|
||||
// load the tokens from encoder.json
|
||||
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
|
||||
|
||||
@ -92,6 +108,18 @@ gpt_vocab::id gpt_sample_top_k_top_p(
|
||||
double temp,
|
||||
std::mt19937 & rng);
|
||||
|
||||
gpt_vocab::id gpt_sample_top_k_top_p_repeat(
|
||||
const gpt_vocab & vocab,
|
||||
const float * logits,
|
||||
const int32_t * last_n_tokens_data,
|
||||
size_t last_n_tokens_data_size,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
int repeat_last_n,
|
||||
float repeat_penalty,
|
||||
std::mt19937 & rng);
|
||||
|
||||
//
|
||||
// Audio utils
|
||||
//
|
||||
|
||||
@ -10,6 +10,10 @@
|
||||
#include <vector>
|
||||
#include <cstring>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
|
||||
// Lowest is red, middle is yellow, highest is green.
|
||||
const std::vector<std::string> k_colors = {
|
||||
@ -148,7 +152,8 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
|
||||
else if (arg == "-f" || arg == "--file") { params.fname_inp.emplace_back(argv[++i]); }
|
||||
else {
|
||||
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
||||
return false;
|
||||
whisper_print_usage(argc, argv, params);
|
||||
exit(0);
|
||||
}
|
||||
}
|
||||
|
||||
@ -423,13 +428,13 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
indent++;
|
||||
};
|
||||
|
||||
auto end_arr = [&](bool end = false) {
|
||||
auto end_arr = [&](bool end) {
|
||||
indent--;
|
||||
doindent();
|
||||
fout << (end ? "]\n" : "},\n");
|
||||
};
|
||||
|
||||
auto start_obj = [&](const char *name = nullptr) {
|
||||
auto start_obj = [&](const char *name) {
|
||||
doindent();
|
||||
if (name) {
|
||||
fout << "\"" << name << "\": {\n";
|
||||
@ -439,7 +444,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
indent++;
|
||||
};
|
||||
|
||||
auto end_obj = [&](bool end = false) {
|
||||
auto end_obj = [&](bool end) {
|
||||
indent--;
|
||||
doindent();
|
||||
fout << (end ? "}\n" : "},\n");
|
||||
@ -450,24 +455,24 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
fout << "\"" << name << "\": ";
|
||||
};
|
||||
|
||||
auto value_s = [&](const char *name, const char *val, bool end = false) {
|
||||
auto value_s = [&](const char *name, const char *val, bool end) {
|
||||
start_value(name);
|
||||
char * val_escaped = escape_double_quotes_and_backslashes(val);
|
||||
fout << "\"" << val_escaped << (end ? "\"\n" : "\",\n");
|
||||
free(val_escaped);
|
||||
};
|
||||
|
||||
auto end_value = [&](bool end = false) {
|
||||
auto end_value = [&](bool end) {
|
||||
fout << (end ? "\n" : ",\n");
|
||||
};
|
||||
|
||||
auto value_i = [&](const char *name, const int64_t val, bool end = false) {
|
||||
auto value_i = [&](const char *name, const int64_t val, bool end) {
|
||||
start_value(name);
|
||||
fout << val;
|
||||
end_value(end);
|
||||
};
|
||||
|
||||
auto value_b = [&](const char *name, const bool val, bool end = false) {
|
||||
auto value_b = [&](const char *name, const bool val, bool end) {
|
||||
start_value(name);
|
||||
fout << (val ? "true" : "false");
|
||||
end_value(end);
|
||||
@ -479,35 +484,35 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
|
||||
start_obj();
|
||||
value_s("systeminfo", whisper_print_system_info());
|
||||
start_obj(nullptr);
|
||||
value_s("systeminfo", whisper_print_system_info(), false);
|
||||
start_obj("model");
|
||||
value_s("type", whisper_model_type_readable(ctx));
|
||||
value_b("multilingual", whisper_is_multilingual(ctx));
|
||||
value_i("vocab", whisper_model_n_vocab(ctx));
|
||||
value_s("type", whisper_model_type_readable(ctx), false);
|
||||
value_b("multilingual", whisper_is_multilingual(ctx), false);
|
||||
value_i("vocab", whisper_model_n_vocab(ctx), false);
|
||||
start_obj("audio");
|
||||
value_i("ctx", whisper_model_n_audio_ctx(ctx));
|
||||
value_i("state", whisper_model_n_audio_state(ctx));
|
||||
value_i("head", whisper_model_n_audio_head(ctx));
|
||||
value_i("ctx", whisper_model_n_audio_ctx(ctx), false);
|
||||
value_i("state", whisper_model_n_audio_state(ctx), false);
|
||||
value_i("head", whisper_model_n_audio_head(ctx), false);
|
||||
value_i("layer", whisper_model_n_audio_layer(ctx), true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
start_obj("text");
|
||||
value_i("ctx", whisper_model_n_text_ctx(ctx));
|
||||
value_i("state", whisper_model_n_text_state(ctx));
|
||||
value_i("head", whisper_model_n_text_head(ctx));
|
||||
value_i("ctx", whisper_model_n_text_ctx(ctx), false);
|
||||
value_i("state", whisper_model_n_text_state(ctx), false);
|
||||
value_i("head", whisper_model_n_text_head(ctx), false);
|
||||
value_i("layer", whisper_model_n_text_layer(ctx), true);
|
||||
end_obj();
|
||||
value_i("mels", whisper_model_n_mels(ctx));
|
||||
end_obj(false);
|
||||
value_i("mels", whisper_model_n_mels(ctx), false);
|
||||
value_i("ftype", whisper_model_ftype(ctx), true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
start_obj("params");
|
||||
value_s("model", params.model.c_str());
|
||||
value_s("language", params.language.c_str());
|
||||
value_s("model", params.model.c_str(), false);
|
||||
value_s("language", params.language.c_str(), false);
|
||||
value_b("translate", params.translate, true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
start_obj("result");
|
||||
value_s("language", whisper_lang_str(whisper_full_lang_id(ctx)), true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
start_arr("transcription");
|
||||
|
||||
const int n_segments = whisper_full_n_segments(ctx);
|
||||
@ -516,15 +521,15 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
|
||||
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
|
||||
|
||||
start_obj();
|
||||
start_obj(nullptr);
|
||||
start_obj("timestamps");
|
||||
value_s("from", to_timestamp(t0, true).c_str());
|
||||
value_s("from", to_timestamp(t0, true).c_str(), false);
|
||||
value_s("to", to_timestamp(t1, true).c_str(), true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
start_obj("offsets");
|
||||
value_i("from", t0 * 10);
|
||||
value_i("from", t0 * 10, false);
|
||||
value_i("to", t1 * 10, true);
|
||||
end_obj();
|
||||
end_obj(false);
|
||||
value_s("text", text, true);
|
||||
end_obj(i == (n_segments - 1));
|
||||
}
|
||||
|
||||
@ -99,17 +99,17 @@ bool whisper_model_quantize(const std::string & fname_inp, const std::string & f
|
||||
fprintf(stderr, "%s: ftype (dst) = %d\n", __func__, ftype_dst);
|
||||
fprintf(stderr, "%s: qntvr (dst) = %d\n", __func__, GGML_QNT_VERSION);
|
||||
|
||||
fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
||||
fout.write((char *) &hparams.n_audio_ctx, sizeof(hparams.n_audio_ctx));
|
||||
fout.write((char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
|
||||
fout.write((char *) &hparams.n_audio_head, sizeof(hparams.n_audio_head));
|
||||
fout.write((char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
|
||||
fout.write((char *) &hparams.n_text_ctx, sizeof(hparams.n_text_ctx));
|
||||
fout.write((char *) &hparams.n_text_state, sizeof(hparams.n_text_state));
|
||||
fout.write((char *) &hparams.n_text_head, sizeof(hparams.n_text_head));
|
||||
fout.write((char *) &hparams.n_text_layer, sizeof(hparams.n_text_layer));
|
||||
fout.write((char *) &hparams.n_mels, sizeof(hparams.n_mels));
|
||||
fout.write((char *) &ftype_dst, sizeof(hparams.ftype));
|
||||
fout.write((const char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
||||
fout.write((const char *) &hparams.n_audio_ctx, sizeof(hparams.n_audio_ctx));
|
||||
fout.write((const char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
|
||||
fout.write((const char *) &hparams.n_audio_head, sizeof(hparams.n_audio_head));
|
||||
fout.write((const char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
|
||||
fout.write((const char *) &hparams.n_text_ctx, sizeof(hparams.n_text_ctx));
|
||||
fout.write((const char *) &hparams.n_text_state, sizeof(hparams.n_text_state));
|
||||
fout.write((const char *) &hparams.n_text_head, sizeof(hparams.n_text_head));
|
||||
fout.write((const char *) &hparams.n_text_layer, sizeof(hparams.n_text_layer));
|
||||
fout.write((const char *) &hparams.n_mels, sizeof(hparams.n_mels));
|
||||
fout.write((const char *) &ftype_dst, sizeof(hparams.ftype));
|
||||
}
|
||||
|
||||
// load mel filters
|
||||
@ -138,15 +138,17 @@ bool whisper_model_quantize(const std::string & fname_inp, const std::string & f
|
||||
// return false;
|
||||
//}
|
||||
|
||||
std::string word;
|
||||
char word[128];
|
||||
|
||||
for (int i = 0; i < n_vocab; i++) {
|
||||
uint32_t len;
|
||||
finp.read ((char *) &len, sizeof(len));
|
||||
fout.write((char *) &len, sizeof(len));
|
||||
|
||||
word.resize(len);
|
||||
finp.read ((char *) word.data(), len);
|
||||
fout.write((char *) word.data(), len);
|
||||
word[len] = '\0';
|
||||
|
||||
finp.read ((char *) word, len);
|
||||
fout.write((char *) word, len);
|
||||
|
||||
vocab.token_to_id[word] = i;
|
||||
vocab.id_to_token[i] = word;
|
||||
|
||||
2818
ggml-cuda.cu
2818
ggml-cuda.cu
File diff suppressed because it is too large
Load Diff
20
ggml-cuda.h
20
ggml-cuda.h
@ -1,10 +1,19 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
struct ggml_tensor_extra_gpu {
|
||||
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
|
||||
};
|
||||
|
||||
void ggml_init_cublas(void);
|
||||
void ggml_cuda_set_tensor_split(const float * tensor_split);
|
||||
|
||||
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
@ -15,8 +24,15 @@ void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
|
||||
void * ggml_cuda_host_malloc(size_t size);
|
||||
void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
void ggml_cuda_transform_tensor(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
|
||||
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
|
||||
|
||||
void ggml_cuda_free_data(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_set_main_device(int main_device);
|
||||
void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
void ggml_cuda_free_scratch(void);
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
void ggml_cl_init(void);
|
||||
|
||||
enum ggml_blas_order {
|
||||
GGML_BLAS_ORDER_ROW_MAJOR = 101,
|
||||
GGML_BLAS_ORDER_COLUMN_MAJOR = 102,
|
||||
};
|
||||
void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
|
||||
|
||||
enum ggml_blas_op {
|
||||
GGML_BLAS_OP_N = 111,
|
||||
GGML_BLAS_OP_T = 112,
|
||||
GGML_BLAS_OP_C = 113,
|
||||
};
|
||||
void * ggml_cl_host_malloc(size_t size);
|
||||
void ggml_cl_host_free(void * ptr);
|
||||
|
||||
void ggml_cl_sgemm_wrapper(const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b, const int m, const int n, const int k, const float alpha, const void *host_a, const int lda, const float *host_b, const int ldb, const float beta, float *host_c, const int ldc, const int btype);
|
||||
void ggml_cl_free_data(const struct ggml_tensor* tensor);
|
||||
|
||||
void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
405
ggml.h
405
ggml.h
@ -198,6 +198,7 @@
|
||||
#define GGML_MAX_PARAMS 256
|
||||
#define GGML_MAX_CONTEXTS 64
|
||||
#define GGML_MAX_OPT 4
|
||||
#define GGML_MAX_NAME 32
|
||||
#define GGML_DEFAULT_N_THREADS 4
|
||||
|
||||
#define GGML_ASSERT(x) \
|
||||
@ -240,6 +241,13 @@ extern "C" {
|
||||
GGML_TYPE_Q5_1 = 7,
|
||||
GGML_TYPE_Q8_0 = 8,
|
||||
GGML_TYPE_Q8_1 = 9,
|
||||
// k-quantizations
|
||||
GGML_TYPE_Q2_K = 10,
|
||||
GGML_TYPE_Q3_K = 11,
|
||||
GGML_TYPE_Q4_K = 12,
|
||||
GGML_TYPE_Q5_K = 13,
|
||||
GGML_TYPE_Q6_K = 14,
|
||||
GGML_TYPE_Q8_K = 15,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
@ -248,7 +256,8 @@ extern "C" {
|
||||
|
||||
enum ggml_backend {
|
||||
GGML_BACKEND_CPU = 0,
|
||||
GGML_BACKEND_CUDA = 1,
|
||||
GGML_BACKEND_GPU = 10,
|
||||
GGML_BACKEND_GPU_SPLIT = 20,
|
||||
};
|
||||
|
||||
// model file types
|
||||
@ -262,6 +271,11 @@ extern "C" {
|
||||
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
|
||||
};
|
||||
|
||||
// available tensor operations:
|
||||
@ -282,12 +296,14 @@ extern "C" {
|
||||
GGML_OP_SUM_ROWS,
|
||||
GGML_OP_MEAN,
|
||||
GGML_OP_REPEAT,
|
||||
GGML_OP_REPEAT_BACK,
|
||||
GGML_OP_ABS,
|
||||
GGML_OP_SGN,
|
||||
GGML_OP_NEG,
|
||||
GGML_OP_STEP,
|
||||
GGML_OP_RELU,
|
||||
GGML_OP_GELU,
|
||||
GGML_OP_GELU_QUICK,
|
||||
GGML_OP_SILU,
|
||||
GGML_OP_SILU_BACK,
|
||||
GGML_OP_NORM, // normalize
|
||||
@ -295,6 +311,7 @@ extern "C" {
|
||||
GGML_OP_RMS_NORM_BACK,
|
||||
|
||||
GGML_OP_MUL_MAT,
|
||||
GGML_OP_OUT_PROD,
|
||||
|
||||
GGML_OP_SCALE,
|
||||
GGML_OP_SET,
|
||||
@ -310,19 +327,31 @@ extern "C" {
|
||||
GGML_OP_DIAG_MASK_INF,
|
||||
GGML_OP_DIAG_MASK_ZERO,
|
||||
GGML_OP_SOFT_MAX,
|
||||
GGML_OP_SOFT_MAX_BACK,
|
||||
GGML_OP_ROPE,
|
||||
GGML_OP_ROPE_BACK,
|
||||
GGML_OP_ALIBI,
|
||||
GGML_OP_CLAMP,
|
||||
GGML_OP_CONV_1D_1S,
|
||||
GGML_OP_CONV_1D_2S,
|
||||
GGML_OP_CONV_1D_S1_PH,
|
||||
GGML_OP_CONV_1D_S2_PH,
|
||||
GGML_OP_CONV_2D_SK_P0,
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_FF,
|
||||
GGML_OP_FLASH_ATTN_BACK,
|
||||
GGML_OP_WIN_PART,
|
||||
GGML_OP_WIN_UNPART,
|
||||
|
||||
GGML_OP_MAP_UNARY,
|
||||
GGML_OP_MAP_BINARY,
|
||||
|
||||
GGML_OP_MAP_CUSTOM1,
|
||||
GGML_OP_MAP_CUSTOM2,
|
||||
GGML_OP_MAP_CUSTOM3,
|
||||
|
||||
GGML_OP_CROSS_ENTROPY_LOSS,
|
||||
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
|
||||
|
||||
GGML_OP_COUNT,
|
||||
};
|
||||
|
||||
@ -371,11 +400,15 @@ extern "C" {
|
||||
|
||||
void * data;
|
||||
|
||||
char name[32];
|
||||
char name[GGML_MAX_NAME];
|
||||
|
||||
char padding[16];
|
||||
void * extra; // extra things e.g. for ggml-cuda.cu
|
||||
|
||||
char padding[4];
|
||||
};
|
||||
|
||||
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
||||
|
||||
// computation graph
|
||||
struct ggml_cgraph {
|
||||
int n_nodes;
|
||||
@ -409,6 +442,25 @@ extern "C" {
|
||||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
|
||||
// compute types
|
||||
enum ggml_task_type {
|
||||
GGML_TASK_INIT = 0,
|
||||
GGML_TASK_COMPUTE,
|
||||
GGML_TASK_FINALIZE,
|
||||
};
|
||||
|
||||
struct ggml_compute_params {
|
||||
enum ggml_task_type type;
|
||||
|
||||
// ith = thread index, nth = number of threads
|
||||
int ith, nth;
|
||||
|
||||
// work buffer for all threads
|
||||
size_t wsize;
|
||||
void * wdata;
|
||||
};
|
||||
|
||||
// misc
|
||||
|
||||
GGML_API void ggml_time_init(void); // call this once at the beginning of the program
|
||||
@ -420,14 +472,17 @@ extern "C" {
|
||||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API int64_t ggml_nelements(const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
|
||||
|
||||
GGML_API int ggml_blck_size (enum ggml_type type);
|
||||
GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
|
||||
GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
|
||||
|
||||
GGML_API const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
|
||||
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
@ -436,14 +491,26 @@ extern "C" {
|
||||
// TODO: temporary until model loading of ggml examples is refactored
|
||||
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
|
||||
|
||||
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
|
||||
// use this to compute the memory overhead of a tensor
|
||||
GGML_API size_t ggml_tensor_overhead(void);
|
||||
|
||||
// main
|
||||
|
||||
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
|
||||
GGML_API void ggml_free(struct ggml_context * ctx);
|
||||
GGML_API void ggml_free(struct ggml_context * ctx);
|
||||
|
||||
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
|
||||
|
||||
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
|
||||
GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx);
|
||||
GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_new_tensor(
|
||||
struct ggml_context * ctx,
|
||||
@ -483,6 +550,8 @@ extern "C" {
|
||||
GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
|
||||
GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
|
||||
GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
|
||||
@ -496,8 +565,9 @@ extern "C" {
|
||||
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name);
|
||||
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
|
||||
GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
|
||||
|
||||
//
|
||||
// operations on tensors with backpropagation
|
||||
@ -522,6 +592,11 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_add1_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_acc(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
@ -545,24 +620,47 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sub_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_mul(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_mul_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_div(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_div_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sqr(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sqr_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sqrt(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sqrt_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_log(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
@ -593,35 +691,76 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_repeat_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_abs(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_abs_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sgn(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_sgn_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_neg(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_neg_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_step(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_step_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_relu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_relu_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// TODO: double-check this computation is correct
|
||||
GGML_API struct ggml_tensor * ggml_gelu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_gelu_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_gelu_quick(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_silu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_silu_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// a - x
|
||||
// b - dy
|
||||
GGML_API struct ggml_tensor * ggml_silu_back(
|
||||
@ -635,10 +774,18 @@ extern "C" {
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_norm_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// a - x
|
||||
// b - dy
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm_back(
|
||||
@ -646,14 +793,22 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// A: m rows, n columns
|
||||
// B: p rows, n columns (i.e. we transpose it internally)
|
||||
// A: n columns, m rows
|
||||
// B: n columns, p rows (i.e. we transpose it internally)
|
||||
// result is m columns, p rows
|
||||
GGML_API struct ggml_tensor * ggml_mul_mat(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// A: m columns, n rows,
|
||||
// B: p columns, n rows,
|
||||
// result is m columns, p rows
|
||||
GGML_API struct ggml_tensor * ggml_out_prod(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
//
|
||||
// operations on tensors without backpropagation
|
||||
//
|
||||
@ -864,6 +1019,17 @@ extern "C" {
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// in-place, returns view(a)
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// rotary position embedding
|
||||
// if mode & 1 == 1, skip n_past elements
|
||||
// if mode & 2 == 1, GPT-NeoX style
|
||||
@ -909,16 +1075,55 @@ extern "C" {
|
||||
float min,
|
||||
float max);
|
||||
|
||||
// padding = 1
|
||||
// TODO: implement general-purpose convolutions
|
||||
// GGML_API struct ggml_tensor * ggml_conv_1d(
|
||||
// struct ggml_context * ctx,
|
||||
// struct ggml_tensor * a,
|
||||
// struct ggml_tensor * b,
|
||||
// int s0
|
||||
// int p0,
|
||||
// int d0);
|
||||
//
|
||||
// GGML_API struct ggml_tensor * ggml_conv_2d(
|
||||
// struct ggml_context * ctx,
|
||||
// struct ggml_tensor * a,
|
||||
// struct ggml_tensor * b,
|
||||
// int s0,
|
||||
// int s1,
|
||||
// int p0,
|
||||
// int p1,
|
||||
// int d0,
|
||||
// int d1);
|
||||
|
||||
// padding = half
|
||||
// TODO: we don't support extra parameters for now
|
||||
// that's why we are hard-coding the stride, padding, and dilation
|
||||
// not great ..
|
||||
GGML_API struct ggml_tensor * ggml_conv_1d_1s(
|
||||
// example:
|
||||
// a: 3 80 768 1
|
||||
// b: 3000 80 1 1
|
||||
// res: 3000 768 1 1
|
||||
// used in whisper
|
||||
GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_conv_1d_2s(
|
||||
// used in whisper
|
||||
GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// kernel size is a->ne[0] x a->ne[1]
|
||||
// stride is equal to kernel size
|
||||
// padding is zero
|
||||
// example:
|
||||
// a: 16 16 3 768
|
||||
// b: 1024 1024 3 1
|
||||
// res: 64 64 768 1
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
@ -930,6 +1135,14 @@ extern "C" {
|
||||
struct ggml_tensor * v,
|
||||
bool masked);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_flash_attn_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
struct ggml_tensor * k,
|
||||
struct ggml_tensor * v,
|
||||
struct ggml_tensor * d,
|
||||
bool masked);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_flash_ff(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
@ -938,21 +1151,106 @@ extern "C" {
|
||||
struct ggml_tensor * c0,
|
||||
struct ggml_tensor * c1);
|
||||
|
||||
// Mapping operations
|
||||
typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
|
||||
// partition into non-overlapping windows with padding if needed
|
||||
// example:
|
||||
// a: 768 64 64 1
|
||||
// w: 14
|
||||
// res: 768 14 14 25
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_win_part(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int w);
|
||||
|
||||
// reverse of ggml_win_part
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_win_unpart(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int w0,
|
||||
int h0,
|
||||
int w);
|
||||
|
||||
// custom operators
|
||||
|
||||
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
|
||||
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
|
||||
|
||||
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
|
||||
// loss function
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c);
|
||||
|
||||
//
|
||||
// automatic differentiation
|
||||
//
|
||||
@ -969,6 +1267,11 @@ extern "C" {
|
||||
GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
|
||||
|
||||
GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
|
||||
GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
|
||||
|
||||
// print info and performance information for the graph
|
||||
GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
|
||||
|
||||
@ -1042,6 +1345,8 @@ extern "C" {
|
||||
struct {
|
||||
int n_iter;
|
||||
|
||||
float sched; // schedule multiplier (fixed, decay or warmup)
|
||||
float decay; // weight decay for AdamW, use 0.0f to disable
|
||||
float alpha; // learning rate
|
||||
float beta1;
|
||||
float beta2;
|
||||
@ -1066,6 +1371,49 @@ extern "C" {
|
||||
} lbfgs;
|
||||
};
|
||||
|
||||
struct ggml_opt_context {
|
||||
struct ggml_context * ctx;
|
||||
struct ggml_opt_params params;
|
||||
|
||||
int iter;
|
||||
int64_t nx; // number of parameter elements
|
||||
|
||||
bool just_initialized;
|
||||
|
||||
struct {
|
||||
struct ggml_tensor * x; // view of the parameters
|
||||
struct ggml_tensor * g1; // gradient
|
||||
struct ggml_tensor * g2; // gradient squared
|
||||
struct ggml_tensor * m; // first moment
|
||||
struct ggml_tensor * v; // second moment
|
||||
struct ggml_tensor * mh; // first moment hat
|
||||
struct ggml_tensor * vh; // second moment hat
|
||||
struct ggml_tensor * pf; // past function values
|
||||
float fx_best;
|
||||
float fx_prev;
|
||||
int n_no_improvement;
|
||||
} adam;
|
||||
|
||||
struct {
|
||||
struct ggml_tensor * x; // current parameters
|
||||
struct ggml_tensor * xp; // previous parameters
|
||||
struct ggml_tensor * g; // current gradient
|
||||
struct ggml_tensor * gp; // previous gradient
|
||||
struct ggml_tensor * d; // search direction
|
||||
struct ggml_tensor * pf; // past function values
|
||||
struct ggml_tensor * lmal; // the L-BFGS memory alpha
|
||||
struct ggml_tensor * lmys; // the L-BFGS memory ys
|
||||
struct ggml_tensor * lms; // the L-BFGS memory s
|
||||
struct ggml_tensor * lmy; // the L-BFGS memory y
|
||||
float fx_best;
|
||||
float step;
|
||||
int j;
|
||||
int k;
|
||||
int end;
|
||||
int n_no_improvement;
|
||||
} lbfgs;
|
||||
};
|
||||
|
||||
GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
|
||||
|
||||
// optimize the function defined by the tensor f
|
||||
@ -1074,6 +1422,27 @@ extern "C" {
|
||||
struct ggml_opt_params params,
|
||||
struct ggml_tensor * f);
|
||||
|
||||
// initialize optimizer context
|
||||
GGML_API void ggml_opt_init(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_opt_context * opt,
|
||||
struct ggml_opt_params params,
|
||||
int64_t nx);
|
||||
|
||||
// continue optimizing the function defined by the tensor f
|
||||
GGML_API enum ggml_opt_result ggml_opt_resume(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_opt_context * opt,
|
||||
struct ggml_tensor * f);
|
||||
|
||||
// continue optimizing the function defined by the tensor f
|
||||
GGML_API enum ggml_opt_result ggml_opt_resume_g(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_opt_context * opt,
|
||||
struct ggml_tensor * f,
|
||||
struct ggml_cgraph * gf,
|
||||
struct ggml_cgraph * gb);
|
||||
|
||||
//
|
||||
// quantization
|
||||
//
|
||||
|
||||
@ -19,6 +19,10 @@
|
||||
#include <regex>
|
||||
#include <random>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
#if defined(GGML_BIG_ENDIAN)
|
||||
#include <bit>
|
||||
|
||||
@ -1468,7 +1472,7 @@ static bool whisper_encode_internal(
|
||||
{
|
||||
wstate.use_buf(ctx0, 1);
|
||||
|
||||
cur = ggml_conv_1d_1s(ctx0, model.e_conv_1_w, mel);
|
||||
cur = ggml_conv_1d_s1_ph(ctx0, model.e_conv_1_w, mel);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0,
|
||||
model.e_conv_1_b,
|
||||
@ -1479,7 +1483,7 @@ static bool whisper_encode_internal(
|
||||
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_conv_1d_2s(ctx0, model.e_conv_2_w, cur);
|
||||
cur = ggml_conv_1d_s2_ph(ctx0, model.e_conv_2_w, cur);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0,
|
||||
model.e_conv_2_b,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user