2b1bc06477
* first attempt at updating to photomaker v2 * continue adding photomaker v2 modules * finishing the last few pieces for photomaker v2; id_embeds need to be done by a manual step and pass as an input file * added a name converter for Photomaker V2; build ok * more debugging underway * failing at cuda mat_mul * updated chunk_half to be more efficient; redo feedforward * fixed a bug: carefully using ggml_view_4d to get chunks of a tensor; strides need to be recalculated or set properly; still failing at soft_max cuda op * redo weight calculation and weight*v * fixed a bug now Photomaker V2 kinds of working * add python script for face detection (Photomaker V2 needs) * updated readme for photomaker * fixed a bug causing PMV1 crashing; both V1 and V2 work * fixed clean_input_ids for PMV2 * fixed a double counting bug in tokenize_with_trigger_token * updated photomaker readme * removed some commented code * improved reconstructing class word free prompt * changed reading id_embed to raw binary using existing load tensor function; this is more efficient than using model load and also makes it easier to work with sd server * minor clean up --------- Co-authored-by: bssrdf <bssrdf@gmail.com>
947 lines
37 KiB
C++
947 lines
37 KiB
C++
#ifndef __CLIP_HPP__
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#define __CLIP_HPP__
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#include "ggml_extend.hpp"
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#include "model.h"
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/*================================================== CLIPTokenizer ===================================================*/
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std::pair<std::unordered_map<std::string, float>, std::string> extract_and_remove_lora(std::string text) {
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std::regex re("<lora:([^:]+):([^>]+)>");
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std::smatch matches;
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std::unordered_map<std::string, float> filename2multiplier;
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while (std::regex_search(text, matches, re)) {
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std::string filename = matches[1].str();
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float multiplier = std::stof(matches[2].str());
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text = std::regex_replace(text, re, "", std::regex_constants::format_first_only);
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if (multiplier == 0.f) {
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continue;
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}
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if (filename2multiplier.find(filename) == filename2multiplier.end()) {
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filename2multiplier[filename] = multiplier;
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} else {
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filename2multiplier[filename] += multiplier;
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}
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}
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return std::make_pair(filename2multiplier, text);
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}
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std::vector<std::pair<int, std::u32string>> bytes_to_unicode() {
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std::vector<std::pair<int, std::u32string>> byte_unicode_pairs;
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std::set<int> byte_set;
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for (int b = static_cast<int>('!'); b <= static_cast<int>('~'); ++b) {
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byte_set.insert(b);
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byte_unicode_pairs.push_back(std::pair<int, std::u32string>(b, unicode_value_to_utf32(b)));
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}
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for (int b = 161; b <= 172; ++b) {
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byte_set.insert(b);
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byte_unicode_pairs.push_back(std::pair<int, std::u32string>(b, unicode_value_to_utf32(b)));
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}
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for (int b = 174; b <= 255; ++b) {
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byte_set.insert(b);
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byte_unicode_pairs.push_back(std::pair<int, std::u32string>(b, unicode_value_to_utf32(b)));
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}
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int n = 0;
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for (int b = 0; b < 256; ++b) {
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if (byte_set.find(b) == byte_set.end()) {
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byte_unicode_pairs.push_back(std::pair<int, std::u32string>(b, unicode_value_to_utf32(n + 256)));
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++n;
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}
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}
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// LOG_DEBUG("byte_unicode_pairs %d", byte_unicode_pairs.size());
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return byte_unicode_pairs;
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}
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// Ref: https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
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typedef std::function<bool(std::string&, std::vector<int32_t>&)> on_new_token_cb_t;
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class CLIPTokenizer {
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private:
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std::map<int, std::u32string> byte_encoder;
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std::map<std::u32string, int> byte_decoder;
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std::map<std::u32string, int> encoder;
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std::map<int, std::u32string> decoder;
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std::map<std::pair<std::u32string, std::u32string>, int> bpe_ranks;
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std::regex pat;
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int encoder_len;
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int bpe_len;
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public:
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const std::string UNK_TOKEN = "<|endoftext|>";
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const std::string BOS_TOKEN = "<|startoftext|>";
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const std::string EOS_TOKEN = "<|endoftext|>";
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const std::string PAD_TOKEN = "<|endoftext|>";
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const int UNK_TOKEN_ID = 49407;
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const int BOS_TOKEN_ID = 49406;
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const int EOS_TOKEN_ID = 49407;
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const int PAD_TOKEN_ID = 49407;
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private:
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static std::string strip(const std::string& str) {
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std::string::size_type start = str.find_first_not_of(" \t\n\r\v\f");
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std::string::size_type end = str.find_last_not_of(" \t\n\r\v\f");
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if (start == std::string::npos) {
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// String contains only whitespace characters
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return "";
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}
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return str.substr(start, end - start + 1);
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}
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static std::string whitespace_clean(std::string text) {
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text = std::regex_replace(text, std::regex(R"(\s+)"), " ");
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text = strip(text);
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return text;
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}
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static std::set<std::pair<std::u32string, std::u32string>> get_pairs(const std::vector<std::u32string>& subwords) {
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std::set<std::pair<std::u32string, std::u32string>> pairs;
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if (subwords.size() == 0) {
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return pairs;
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}
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std::u32string prev_subword = subwords[0];
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for (int i = 1; i < subwords.size(); i++) {
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std::u32string subword = subwords[i];
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std::pair<std::u32string, std::u32string> pair(prev_subword, subword);
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pairs.insert(pair);
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prev_subword = subword;
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}
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return pairs;
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}
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public:
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CLIPTokenizer(int pad_token_id = 49407, const std::string& merges_utf8_str = "")
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: PAD_TOKEN_ID(pad_token_id) {
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if (merges_utf8_str.size() > 0) {
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load_from_merges(merges_utf8_str);
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} else {
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load_from_merges(ModelLoader::load_merges());
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}
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}
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void load_from_merges(const std::string& merges_utf8_str) {
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auto byte_unicode_pairs = bytes_to_unicode();
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// printf("byte_unicode_pairs have %lu pairs \n", byte_unicode_pairs.size());
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byte_encoder = std::map<int, std::u32string>(byte_unicode_pairs.begin(), byte_unicode_pairs.end());
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for (auto& pair : byte_unicode_pairs) {
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byte_decoder[pair.second] = pair.first;
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}
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// for (auto & pair: byte_unicode_pairs) {
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// std::cout << pair.first << ": " << pair.second << std::endl;
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// }
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std::vector<std::u32string> merges;
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size_t start = 0;
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size_t pos;
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std::u32string merges_utf32_str = utf8_to_utf32(merges_utf8_str);
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while ((pos = merges_utf32_str.find('\n', start)) != std::string::npos) {
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merges.push_back(merges_utf32_str.substr(start, pos - start));
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start = pos + 1;
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}
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// LOG_DEBUG("merges size %llu", merges.size());
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GGML_ASSERT(merges.size() == 48895);
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merges = std::vector<std::u32string>(merges.begin() + 1, merges.end());
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std::vector<std::pair<std::u32string, std::u32string>> merge_pairs;
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for (const auto& merge : merges) {
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size_t space_pos = merge.find(' ');
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merge_pairs.emplace_back(merge.substr(0, space_pos), merge.substr(space_pos + 1));
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// LOG_DEBUG("%s", utf32_to_utf8(merge.substr(space_pos + 1)).c_str());
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// printf("%s :: %s | %s \n", utf32_to_utf8(merge).c_str(), utf32_to_utf8(merge.substr(0, space_pos)).c_str(),
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// utf32_to_utf8(merge.substr(space_pos + 1)).c_str());
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}
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std::vector<std::u32string> vocab;
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for (const auto& pair : byte_unicode_pairs) {
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vocab.push_back(pair.second);
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}
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for (const auto& pair : byte_unicode_pairs) {
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vocab.push_back(pair.second + utf8_to_utf32("</w>"));
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}
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for (const auto& merge : merge_pairs) {
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vocab.push_back(merge.first + merge.second);
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}
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vocab.push_back(utf8_to_utf32("<|startoftext|>"));
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vocab.push_back(utf8_to_utf32("<|endoftext|>"));
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LOG_DEBUG("vocab size: %llu", vocab.size());
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int i = 0;
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for (const auto& token : vocab) {
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encoder[token] = i;
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decoder[i] = token;
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i++;
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}
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encoder_len = i;
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auto it = encoder.find(utf8_to_utf32("img</w>"));
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if (it != encoder.end()) {
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LOG_DEBUG(" trigger word img already in vocab");
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} else {
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LOG_DEBUG(" trigger word img not in vocab yet");
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}
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int rank = 0;
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for (const auto& merge : merge_pairs) {
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bpe_ranks[merge] = rank++;
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}
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bpe_len = rank;
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};
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void add_token(const std::string& text) {
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std::u32string token = utf8_to_utf32(text);
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auto it = encoder.find(token);
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if (it != encoder.end()) {
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encoder[token] = encoder_len;
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decoder[encoder_len] = token;
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encoder_len++;
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}
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}
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std::u32string bpe(const std::u32string& token) {
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std::vector<std::u32string> word;
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for (int i = 0; i < token.size() - 1; i++) {
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word.emplace_back(1, token[i]);
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}
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word.push_back(token.substr(token.size() - 1) + utf8_to_utf32("</w>"));
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std::set<std::pair<std::u32string, std::u32string>> pairs = get_pairs(word);
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if (pairs.empty()) {
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return token + utf8_to_utf32("</w>");
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}
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while (true) {
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auto min_pair_iter = std::min_element(pairs.begin(),
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pairs.end(),
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[&](const std::pair<std::u32string, std::u32string>& a,
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const std::pair<std::u32string, std::u32string>& b) {
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if (bpe_ranks.find(a) == bpe_ranks.end()) {
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return false;
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} else if (bpe_ranks.find(b) == bpe_ranks.end()) {
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return true;
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}
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return bpe_ranks.at(a) < bpe_ranks.at(b);
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});
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const std::pair<std::u32string, std::u32string>& bigram = *min_pair_iter;
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if (bpe_ranks.find(bigram) == bpe_ranks.end()) {
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break;
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}
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std::u32string first = bigram.first;
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std::u32string second = bigram.second;
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std::vector<std::u32string> new_word;
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int32_t i = 0;
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while (i < word.size()) {
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auto it = std::find(word.begin() + i, word.end(), first);
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if (it == word.end()) {
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new_word.insert(new_word.end(), word.begin() + i, word.end());
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break;
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}
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new_word.insert(new_word.end(), word.begin() + i, it);
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i = static_cast<int32_t>(std::distance(word.begin(), it));
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if (word[i] == first && i < static_cast<int32_t>(word.size()) - 1 && word[i + 1] == second) {
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new_word.push_back(first + second);
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i += 2;
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} else {
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new_word.push_back(word[i]);
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i += 1;
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}
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}
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word = new_word;
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if (word.size() == 1) {
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break;
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}
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pairs = get_pairs(word);
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}
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std::u32string result;
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for (int i = 0; i < word.size(); i++) {
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result += word[i];
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if (i != word.size() - 1) {
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result += utf8_to_utf32(" ");
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}
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}
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return result;
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}
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std::vector<int> tokenize(std::string text,
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on_new_token_cb_t on_new_token_cb,
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size_t max_length = 0,
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bool padding = false) {
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std::vector<int32_t> tokens = encode(text, on_new_token_cb);
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tokens.insert(tokens.begin(), BOS_TOKEN_ID);
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if (max_length > 0) {
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if (tokens.size() > max_length - 1) {
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tokens.resize(max_length - 1);
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tokens.push_back(EOS_TOKEN_ID);
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} else {
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tokens.push_back(EOS_TOKEN_ID);
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if (padding) {
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tokens.insert(tokens.end(), max_length - tokens.size(), PAD_TOKEN_ID);
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}
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}
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}
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return tokens;
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}
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void pad_tokens(std::vector<int>& tokens,
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std::vector<float>& weights,
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size_t max_length = 0,
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bool padding = false) {
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if (max_length > 0 && padding) {
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size_t n = std::ceil(tokens.size() * 1.0 / (max_length - 2));
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if (n == 0) {
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n = 1;
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}
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size_t length = max_length * n;
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LOG_DEBUG("token length: %llu", length);
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std::vector<int> new_tokens;
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std::vector<float> new_weights;
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new_tokens.push_back(BOS_TOKEN_ID);
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new_weights.push_back(1.0);
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int token_idx = 0;
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for (int i = 1; i < length; i++) {
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if (token_idx >= tokens.size()) {
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break;
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}
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if (i % max_length == 0) {
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new_tokens.push_back(BOS_TOKEN_ID);
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new_weights.push_back(1.0);
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} else if (i % max_length == max_length - 1) {
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new_tokens.push_back(EOS_TOKEN_ID);
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new_weights.push_back(1.0);
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} else {
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new_tokens.push_back(tokens[token_idx]);
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new_weights.push_back(weights[token_idx]);
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token_idx++;
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}
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}
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new_tokens.push_back(EOS_TOKEN_ID);
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new_weights.push_back(1.0);
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tokens = new_tokens;
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weights = new_weights;
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if (padding) {
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tokens.insert(tokens.end(), length - tokens.size(), PAD_TOKEN_ID);
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weights.insert(weights.end(), length - weights.size(), 1.0);
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}
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}
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}
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std::string clean_up_tokenization(std::string &text){
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std::regex pattern(R"( ,)");
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// Replace " ," with ","
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std::string result = std::regex_replace(text, pattern, ",");
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return result;
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}
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std::string decode(const std::vector<int>& tokens) {
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std::string text = "";
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for (int t : tokens) {
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if (t == 49406 || t == 49407)
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continue;
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std::u32string ts = decoder[t];
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// printf("%d, %s \n", t, utf32_to_utf8(ts).c_str());
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std::string s = utf32_to_utf8(ts);
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if (s.length() >= 4 ){
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if(ends_with(s, "</w>")) {
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text += s.replace(s.length() - 4, s.length() - 1, "") + " ";
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}else{
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text += s;
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}
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} else {
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text += " " + s;
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}
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}
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// std::vector<unsigned char> bytes;
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// for (auto c : text){
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// bytes.push_back(byte_decoder[c]);
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// }
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// std::string s((char *)bytes.data());
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// std::string s = "";
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text = clean_up_tokenization(text);
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return trim(text);
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}
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std::vector<int> encode(std::string text, on_new_token_cb_t on_new_token_cb) {
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std::string original_text = text;
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std::vector<int32_t> bpe_tokens;
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text = whitespace_clean(text);
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std::transform(text.begin(), text.end(), text.begin(), [](unsigned char c) { return std::tolower(c); });
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std::regex pat(R"(<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[[:alpha:]]+|[[:digit:]]|[^[:space:][:alpha:][:digit:]]+)",
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std::regex::icase);
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std::smatch matches;
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std::string str = text;
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std::vector<std::string> token_strs;
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while (std::regex_search(str, matches, pat)) {
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bool skip = on_new_token_cb(str, bpe_tokens);
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if (skip) {
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continue;
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}
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for (auto& token : matches) {
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std::string token_str = token.str();
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std::u32string utf32_token;
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for (int i = 0; i < token_str.length(); i++) {
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unsigned char b = token_str[i];
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utf32_token += byte_encoder[b];
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}
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auto bpe_strs = bpe(utf32_token);
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size_t start = 0;
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size_t pos;
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while ((pos = bpe_strs.find(' ', start)) != std::u32string::npos) {
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auto bpe_str = bpe_strs.substr(start, pos - start);
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bpe_tokens.push_back(encoder[bpe_str]);
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token_strs.push_back(utf32_to_utf8(bpe_str));
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start = pos + 1;
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}
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auto bpe_str = bpe_strs.substr(start, bpe_strs.size() - start);
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bpe_tokens.push_back(encoder[bpe_str]);
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token_strs.push_back(utf32_to_utf8(bpe_str));
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}
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str = matches.suffix();
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}
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std::stringstream ss;
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ss << "[";
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for (auto token : token_strs) {
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ss << "\"" << token << "\", ";
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}
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ss << "]";
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// LOG_DEBUG("split prompt \"%s\" to tokens %s", original_text.c_str(), ss.str().c_str());
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// printf("split prompt \"%s\" to tokens %s \n", original_text.c_str(), ss.str().c_str());
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return bpe_tokens;
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}
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};
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/*================================================ FrozenCLIPEmbedder ================================================*/
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// Ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py
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struct CLIPMLP : public GGMLBlock {
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protected:
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bool use_gelu;
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|
public:
|
|
CLIPMLP(int64_t d_model, int64_t intermediate_size) {
|
|
blocks["fc1"] = std::shared_ptr<GGMLBlock>(new Linear(d_model, intermediate_size));
|
|
blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(intermediate_size, d_model));
|
|
|
|
if (d_model == 1024 || d_model == 1280) { // SD 2.x
|
|
use_gelu = true;
|
|
} else { // SD 1.x
|
|
use_gelu = false;
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
|
|
// x: [N, n_token, d_model]
|
|
auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
|
|
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
|
|
|
|
x = fc1->forward(ctx, x);
|
|
if (use_gelu) {
|
|
x = ggml_gelu_inplace(ctx, x);
|
|
} else {
|
|
x = ggml_gelu_quick_inplace(ctx, x);
|
|
}
|
|
x = fc2->forward(ctx, x);
|
|
return x;
|
|
}
|
|
};
|
|
|
|
struct CLIPLayer : public GGMLBlock {
|
|
protected:
|
|
int64_t d_model; // hidden_size/embed_dim
|
|
int64_t n_head;
|
|
int64_t intermediate_size;
|
|
|
|
public:
|
|
CLIPLayer(int64_t d_model,
|
|
int64_t n_head,
|
|
int64_t intermediate_size)
|
|
: d_model(d_model),
|
|
n_head(n_head),
|
|
intermediate_size(intermediate_size) {
|
|
blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head, true, true));
|
|
|
|
blocks["layer_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
|
|
blocks["layer_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
|
|
|
|
blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = true) {
|
|
// x: [N, n_token, d_model]
|
|
auto self_attn = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
|
|
auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
|
|
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
|
|
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
|
|
|
|
x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
|
|
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
|
|
return x;
|
|
}
|
|
};
|
|
|
|
struct CLIPEncoder : public GGMLBlock {
|
|
protected:
|
|
int64_t n_layer;
|
|
|
|
public:
|
|
CLIPEncoder(int64_t n_layer,
|
|
int64_t d_model,
|
|
int64_t n_head,
|
|
int64_t intermediate_size)
|
|
: n_layer(n_layer) {
|
|
for (int i = 0; i < n_layer; i++) {
|
|
std::string name = "layers." + std::to_string(i);
|
|
blocks[name] = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size));
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, int clip_skip = -1, bool mask = true) {
|
|
// x: [N, n_token, d_model]
|
|
int layer_idx = n_layer - 1;
|
|
// LOG_DEBUG("clip_skip %d", clip_skip);
|
|
if (clip_skip > 0) {
|
|
layer_idx = n_layer - clip_skip;
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; i++) {
|
|
// LOG_DEBUG("layer %d", i);
|
|
if (i == layer_idx + 1) {
|
|
break;
|
|
}
|
|
std::string name = "layers." + std::to_string(i);
|
|
auto layer = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
|
|
x = layer->forward(ctx, x, mask); // [N, n_token, d_model]
|
|
// LOG_DEBUG("layer %d", i);
|
|
}
|
|
return x;
|
|
}
|
|
};
|
|
|
|
class CLIPEmbeddings : public GGMLBlock {
|
|
protected:
|
|
int64_t embed_dim;
|
|
int64_t vocab_size;
|
|
int64_t num_positions;
|
|
|
|
void init_params(struct ggml_context* ctx, ggml_type wtype) {
|
|
params["token_embedding.weight"] = ggml_new_tensor_2d(ctx, wtype, embed_dim, vocab_size);
|
|
params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
|
|
}
|
|
|
|
public:
|
|
CLIPEmbeddings(int64_t embed_dim,
|
|
int64_t vocab_size = 49408,
|
|
int64_t num_positions = 77)
|
|
: embed_dim(embed_dim),
|
|
vocab_size(vocab_size),
|
|
num_positions(num_positions) {
|
|
}
|
|
|
|
struct ggml_tensor* get_token_embed_weight() {
|
|
return params["token_embedding.weight"];
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx,
|
|
struct ggml_tensor* input_ids,
|
|
struct ggml_tensor* custom_embed_weight) {
|
|
// input_ids: [N, n_token]
|
|
auto token_embed_weight = params["token_embedding.weight"];
|
|
auto position_embed_weight = params["position_embedding.weight"];
|
|
|
|
GGML_ASSERT(input_ids->ne[0] == position_embed_weight->ne[1]);
|
|
input_ids = ggml_reshape_3d(ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
|
|
auto token_embedding = ggml_get_rows(ctx, custom_embed_weight != NULL ? custom_embed_weight : token_embed_weight, input_ids);
|
|
token_embedding = ggml_reshape_3d(ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
|
|
|
|
// token_embedding + position_embedding
|
|
auto x = ggml_add(ctx,
|
|
token_embedding,
|
|
position_embed_weight); // [N, n_token, embed_dim]
|
|
return x;
|
|
}
|
|
};
|
|
|
|
class CLIPVisionEmbeddings : public GGMLBlock {
|
|
protected:
|
|
int64_t embed_dim;
|
|
int64_t num_channels;
|
|
int64_t patch_size;
|
|
int64_t image_size;
|
|
int64_t num_patches;
|
|
int64_t num_positions;
|
|
|
|
void init_params(struct ggml_context* ctx, ggml_type wtype) {
|
|
params["patch_embedding.weight"] = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, patch_size, patch_size, num_channels, embed_dim);
|
|
params["class_embedding"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, embed_dim);
|
|
params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
|
|
}
|
|
|
|
public:
|
|
CLIPVisionEmbeddings(int64_t embed_dim,
|
|
int64_t num_channels = 3,
|
|
int64_t patch_size = 14,
|
|
int64_t image_size = 224)
|
|
: embed_dim(embed_dim),
|
|
num_channels(num_channels),
|
|
patch_size(patch_size),
|
|
image_size(image_size) {
|
|
num_patches = (image_size / patch_size) * (image_size / patch_size);
|
|
num_positions = num_patches + 1;
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
|
|
// pixel_values: [N, num_channels, image_size, image_size]
|
|
// return: [N, num_positions, embed_dim]
|
|
GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
|
|
|
|
auto patch_embed_weight = params["patch_embedding.weight"];
|
|
auto class_embed_weight = params["class_embedding"];
|
|
auto position_embed_weight = params["position_embedding.weight"];
|
|
|
|
// concat(patch_embedding, class_embedding) + position_embedding
|
|
struct ggml_tensor* patch_embedding;
|
|
int64_t N = pixel_values->ne[3];
|
|
patch_embedding = ggml_nn_conv_2d(ctx, pixel_values, patch_embed_weight, NULL, patch_size, patch_size); // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
|
|
patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N); // [N, embed_dim, num_patches]
|
|
patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3)); // [N, num_patches, embed_dim]
|
|
patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N); // [N, num_patches, embed_dim, 1]
|
|
|
|
struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, N);
|
|
class_embedding = ggml_repeat(ctx, class_embed_weight, class_embedding); // [N, embed_dim]
|
|
class_embedding = ggml_reshape_4d(ctx, class_embedding, 1, embed_dim, 1, N); // [N, 1, embed_dim, 1]
|
|
|
|
struct ggml_tensor* x = ggml_concat(ctx, class_embedding, patch_embedding, 2); // [N, num_positions, embed_dim, 1]
|
|
x = ggml_reshape_3d(ctx, x, embed_dim, num_positions, N); // [N, num_positions, embed_dim]
|
|
x = ggml_add(ctx, x, position_embed_weight);
|
|
return x; // [N, num_positions, embed_dim]
|
|
}
|
|
};
|
|
|
|
// OPENAI_CLIP_VIT_L_14: https://huggingface.co/openai/clip-vit-large-patch14/blob/main/config.json
|
|
// OPEN_CLIP_VIT_H_14: https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/blob/main/config.json
|
|
// OPEN_CLIP_VIT_BIGG_14: https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/blob/main/config.json (CLIPTextModelWithProjection)
|
|
|
|
enum CLIPVersion {
|
|
OPENAI_CLIP_VIT_L_14, // SD 1.x and SDXL
|
|
OPEN_CLIP_VIT_H_14, // SD 2.x
|
|
OPEN_CLIP_VIT_BIGG_14, // SDXL
|
|
};
|
|
|
|
class CLIPTextModel : public GGMLBlock {
|
|
protected:
|
|
void init_params(struct ggml_context* ctx, ggml_type wtype) {
|
|
if (version == OPEN_CLIP_VIT_BIGG_14) {
|
|
params["text_projection"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, projection_dim, hidden_size);
|
|
}
|
|
}
|
|
|
|
public:
|
|
CLIPVersion version = OPENAI_CLIP_VIT_L_14;
|
|
// network hparams
|
|
int32_t vocab_size = 49408;
|
|
int32_t n_token = 77; // max_position_embeddings
|
|
int32_t hidden_size = 768;
|
|
int32_t intermediate_size = 3072;
|
|
int32_t n_head = 12;
|
|
int32_t n_layer = 12; // num_hidden_layers
|
|
int32_t projection_dim = 1280; // only for OPEN_CLIP_VIT_BIGG_14
|
|
int32_t clip_skip = -1;
|
|
bool with_final_ln = true;
|
|
|
|
CLIPTextModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14,
|
|
int clip_skip_value = -1,
|
|
bool with_final_ln = true)
|
|
: version(version), with_final_ln(with_final_ln) {
|
|
if (version == OPEN_CLIP_VIT_H_14) {
|
|
hidden_size = 1024;
|
|
intermediate_size = 4096;
|
|
n_head = 16;
|
|
n_layer = 24;
|
|
} else if (version == OPEN_CLIP_VIT_BIGG_14) { // CLIPTextModelWithProjection
|
|
hidden_size = 1280;
|
|
intermediate_size = 5120;
|
|
n_head = 20;
|
|
n_layer = 32;
|
|
}
|
|
set_clip_skip(clip_skip_value);
|
|
|
|
blocks["embeddings"] = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token));
|
|
blocks["encoder"] = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
|
|
blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
|
|
}
|
|
|
|
void set_clip_skip(int skip) {
|
|
if (skip <= 0) {
|
|
return;
|
|
}
|
|
clip_skip = skip;
|
|
}
|
|
|
|
struct ggml_tensor* get_token_embed_weight() {
|
|
auto embeddings = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
|
|
return embeddings->get_token_embed_weight();
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx,
|
|
struct ggml_tensor* input_ids,
|
|
struct ggml_tensor* tkn_embeddings,
|
|
size_t max_token_idx = 0,
|
|
bool return_pooled = false) {
|
|
// input_ids: [N, n_token]
|
|
auto embeddings = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
|
|
auto encoder = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
|
|
auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);
|
|
|
|
auto x = embeddings->forward(ctx, input_ids, tkn_embeddings); // [N, n_token, hidden_size]
|
|
x = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
|
|
if (return_pooled || with_final_ln) {
|
|
x = final_layer_norm->forward(ctx, x);
|
|
}
|
|
|
|
if (return_pooled) {
|
|
auto text_projection = params["text_projection"];
|
|
ggml_tensor* pooled = ggml_view_1d(ctx, x, hidden_size, x->nb[1] * max_token_idx);
|
|
if (text_projection != NULL) {
|
|
pooled = ggml_nn_linear(ctx, pooled, text_projection, NULL);
|
|
} else {
|
|
LOG_DEBUG("Missing text_projection matrix, assuming identity...");
|
|
}
|
|
return pooled; // [hidden_size, 1, 1]
|
|
}
|
|
|
|
return x; // [N, n_token, hidden_size]
|
|
}
|
|
};
|
|
|
|
class CLIPVisionModel : public GGMLBlock {
|
|
public:
|
|
// network hparams
|
|
int32_t num_channels = 3;
|
|
int32_t patch_size = 14;
|
|
int32_t image_size = 224;
|
|
int32_t num_positions = 257; // (image_size / patch_size)^2 + 1
|
|
int32_t hidden_size = 1024;
|
|
int32_t intermediate_size = 4096;
|
|
int32_t n_head = 16;
|
|
int32_t n_layer = 24;
|
|
|
|
public:
|
|
CLIPVisionModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14) {
|
|
if (version == OPEN_CLIP_VIT_H_14) {
|
|
hidden_size = 1280;
|
|
intermediate_size = 5120;
|
|
n_head = 16;
|
|
n_layer = 32;
|
|
} else if (version == OPEN_CLIP_VIT_BIGG_14) {
|
|
hidden_size = 1664;
|
|
intermediate_size = 8192;
|
|
n_head = 16;
|
|
n_layer = 48;
|
|
}
|
|
|
|
blocks["embeddings"] = std::shared_ptr<GGMLBlock>(new CLIPVisionEmbeddings(hidden_size, num_channels, patch_size, image_size));
|
|
blocks["pre_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
|
|
blocks["encoder"] = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
|
|
blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values,
|
|
bool return_pooled = true) {
|
|
// pixel_values: [N, num_channels, image_size, image_size]
|
|
auto embeddings = std::dynamic_pointer_cast<CLIPVisionEmbeddings>(blocks["embeddings"]);
|
|
auto pre_layernorm = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_layernorm"]);
|
|
auto encoder = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
|
|
auto post_layernorm = std::dynamic_pointer_cast<LayerNorm>(blocks["post_layernorm"]);
|
|
|
|
auto x = embeddings->forward(ctx, pixel_values); // [N, num_positions, embed_dim]
|
|
x = pre_layernorm->forward(ctx, x);
|
|
x = encoder->forward(ctx, x, -1, false);
|
|
// print_ggml_tensor(x, true, "ClipVisionModel x: ");
|
|
auto last_hidden_state = x;
|
|
x = post_layernorm->forward(ctx, x); // [N, n_token, hidden_size]
|
|
|
|
GGML_ASSERT(x->ne[3] == 1);
|
|
if (return_pooled) {
|
|
ggml_tensor* pooled = ggml_cont(ctx, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
|
|
return pooled; // [N, hidden_size]
|
|
} else {
|
|
// return x; // [N, n_token, hidden_size]
|
|
return last_hidden_state; // [N, n_token, hidden_size]
|
|
}
|
|
}
|
|
};
|
|
|
|
class CLIPProjection : public UnaryBlock {
|
|
protected:
|
|
int64_t in_features;
|
|
int64_t out_features;
|
|
bool transpose_weight;
|
|
|
|
void init_params(struct ggml_context* ctx, ggml_type wtype) {
|
|
if (transpose_weight) {
|
|
LOG_ERROR("transpose_weight");
|
|
params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
|
|
} else {
|
|
params["weight"] = ggml_new_tensor_2d(ctx, wtype, in_features, out_features);
|
|
}
|
|
}
|
|
|
|
public:
|
|
CLIPProjection(int64_t in_features,
|
|
int64_t out_features,
|
|
bool transpose_weight = false)
|
|
: in_features(in_features),
|
|
out_features(out_features),
|
|
transpose_weight(transpose_weight) {}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
|
|
struct ggml_tensor* w = params["weight"];
|
|
if (transpose_weight) {
|
|
w = ggml_cont(ctx, ggml_transpose(ctx, w));
|
|
}
|
|
return ggml_nn_linear(ctx, x, w, NULL);
|
|
}
|
|
};
|
|
|
|
class CLIPVisionModelProjection : public GGMLBlock {
|
|
public:
|
|
int32_t hidden_size = 1024;
|
|
int32_t projection_dim = 768;
|
|
int32_t image_size = 224;
|
|
|
|
public:
|
|
CLIPVisionModelProjection(CLIPVersion version = OPENAI_CLIP_VIT_L_14,
|
|
bool transpose_proj_w = false) {
|
|
if (version == OPEN_CLIP_VIT_H_14) {
|
|
hidden_size = 1280;
|
|
projection_dim = 1024;
|
|
} else if (version == OPEN_CLIP_VIT_BIGG_14) {
|
|
hidden_size = 1664;
|
|
}
|
|
|
|
blocks["vision_model"] = std::shared_ptr<GGMLBlock>(new CLIPVisionModel(version));
|
|
blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
|
|
}
|
|
|
|
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
|
|
// pixel_values: [N, num_channels, image_size, image_size]
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// return: [N, projection_dim]
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auto vision_model = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
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auto visual_projection = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
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auto x = vision_model->forward(ctx, pixel_values); // [N, hidden_size]
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x = visual_projection->forward(ctx, x); // [N, projection_dim]
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return x; // [N, projection_dim]
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}
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};
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struct CLIPTextModelRunner : public GGMLRunner {
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CLIPTextModel model;
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CLIPTextModelRunner(ggml_backend_t backend,
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ggml_type wtype,
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CLIPVersion version = OPENAI_CLIP_VIT_L_14,
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int clip_skip_value = 1,
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bool with_final_ln = true)
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: GGMLRunner(backend, wtype), model(version, clip_skip_value, with_final_ln) {
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model.init(params_ctx, wtype);
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}
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|
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std::string get_desc() {
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return "clip";
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}
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|
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void set_clip_skip(int clip_skip) {
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model.set_clip_skip(clip_skip);
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}
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|
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void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
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model.get_param_tensors(tensors, prefix);
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}
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|
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struct ggml_tensor* forward(struct ggml_context* ctx,
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struct ggml_tensor* input_ids,
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|
struct ggml_tensor* embeddings,
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|
size_t max_token_idx = 0,
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bool return_pooled = false) {
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|
size_t N = input_ids->ne[1];
|
|
size_t n_token = input_ids->ne[0];
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|
if (input_ids->ne[0] > model.n_token) {
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GGML_ASSERT(input_ids->ne[0] % model.n_token == 0);
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input_ids = ggml_reshape_2d(ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
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}
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|
|
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return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled);
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|
}
|
|
|
|
struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
|
|
int num_custom_embeddings = 0,
|
|
void* custom_embeddings_data = NULL,
|
|
size_t max_token_idx = 0,
|
|
bool return_pooled = false) {
|
|
struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
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|
|
|
input_ids = to_backend(input_ids);
|
|
|
|
struct ggml_tensor* embeddings = NULL;
|
|
|
|
if (num_custom_embeddings > 0 && custom_embeddings_data != NULL) {
|
|
auto custom_embeddings = ggml_new_tensor_2d(compute_ctx,
|
|
wtype,
|
|
model.hidden_size,
|
|
num_custom_embeddings);
|
|
set_backend_tensor_data(custom_embeddings, custom_embeddings_data);
|
|
|
|
auto token_embed_weight = model.get_token_embed_weight();
|
|
// concatenate custom embeddings
|
|
embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
|
|
}
|
|
|
|
struct ggml_tensor* hidden_states = forward(compute_ctx, input_ids, embeddings, max_token_idx, return_pooled);
|
|
|
|
ggml_build_forward_expand(gf, hidden_states);
|
|
|
|
return gf;
|
|
}
|
|
|
|
void compute(const int n_threads,
|
|
struct ggml_tensor* input_ids,
|
|
int num_custom_embeddings,
|
|
void* custom_embeddings_data,
|
|
size_t max_token_idx,
|
|
bool return_pooled,
|
|
ggml_tensor** output,
|
|
ggml_context* output_ctx = NULL) {
|
|
auto get_graph = [&]() -> struct ggml_cgraph* {
|
|
return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled);
|
|
};
|
|
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
|
|
}
|
|
};
|
|
|
|
#endif // __CLIP_HPP__
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