Revert "New tokenizer implementation for MPT and GPT-J"

This reverts commit bbcee1ced5.

.codespellrc

@@ -1,4 +1,4 @@
 [codespell]
-skip = .git,*.pdf,*.svg,*_tokenizer_config.h
+skip = .git,*.pdf,*.svg
 #
 # ignore-words-list =

gpt4all-backend/CMakeLists.txt

@@ -23,7 +23,6 @@ set(LLMODEL_VERSION "${LLMODEL_VERSION_MAJOR}.${LLMODEL_VERSION_MINOR}.${LLMODEL
 project(llmodel VERSION ${LLMODEL_VERSION} LANGUAGES CXX C)
 
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_STANDARD 17)
 
 set(LLAMA_BUILD_EXAMPLES ON CACHE BOOL "llama: build examples" FORCE)
 set(BUILD_SHARED_LIBS ON FORCE)
@@ -35,7 +34,6 @@ if (GPT4ALL_AVX_ONLY)
     set(LLAMA_FMA  OFF CACHE BOOL "llama: enable FMA" FORCE)
 endif()
 
-find_package(ICU REQUIRED COMPONENTS uc i18n)
 add_subdirectory(llama.cpp)
 
 add_library(llmodel
@@ -43,14 +41,12 @@ add_library(llmodel
     llamamodel.h llamamodel.cpp
     llama.cpp/examples/common.cpp
     llmodel.h llmodel_c.h llmodel_c.cpp
-    mpt.h mpt.cpp tokenizer/bpe.cpp tokenizer/bpe.h
+    mpt.h mpt.cpp
-    tokenizer/mpt_tokenizer_config.h tokenizer/gptj_tokenizer_config.h
     utils.h utils.cpp
 )
 
 target_link_libraries(llmodel
-    PRIVATE llama
+    PRIVATE llama)
-    PUBLIC ICU::uc ICU::i18n)
 
 set_target_properties(llmodel PROPERTIES
                               VERSION ${PROJECT_VERSION}

gpt4all-backend/gptj.cpp

@@ -7,7 +7,6 @@
 #include <cmath>
 #include <cstdio>
 #include <cstring>
-#include <filesystem>
 #include <fstream>
 #include <map>
 #include <string>
@@ -861,8 +860,6 @@ bool GPTJ::loadModel(const std::string &modelPath) {
     d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
     d_ptr->modelLoaded = true;
     fflush(stdout);
-    
-    get_bpecpp_tokenizer(TokenizerType::GPTJ, m_bpe, m_tokav);
     return true;
 }
 
@@ -918,7 +915,7 @@ void GPTJ::prompt(const std::string &prompt,
     int64_t t_prompt_us = 0;
 
     // tokenize the prompt
-    std::vector<uint32_t> embd_inp = m_tokav->encode(prompt, *m_bpe);
+    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
 
     // save the context size
     promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
@@ -1035,7 +1032,7 @@ void GPTJ::prompt(const std::string &prompt,
         if (id == 50256 /*end of text*/)
             goto stop_generating;
 
-        const std::string str = m_tokav->decode({(uint32_t) id}, *m_bpe, true, false);
+        const std::string str = d_ptr->vocab.id_to_token[id];
 
         // Check if the provided str is part of our reverse prompts
         bool foundPartialReversePrompt = false;
@@ -1065,8 +1062,7 @@ void GPTJ::prompt(const std::string &prompt,
             if (promptCtx.tokens.size() == promptCtx.n_ctx)
                 promptCtx.tokens.erase(promptCtx.tokens.begin());
             promptCtx.tokens.push_back(t);
-            const std::string decoded = m_tokav->decode({(uint32_t) t}, *m_bpe, true, false);
+            if (!responseCallback(t, d_ptr->vocab.id_to_token[t]))
-            if (!responseCallback(t, decoded))
                 goto stop_generating;
         }
         cachedTokens.clear();

gpt4all-backend/gptj.h

@@ -5,7 +5,6 @@
 #include <functional>
 #include <vector>
 #include "llmodel.h"
-#include "tokenizer/bpe.h"
 
 class GPTJPrivate;
 class GPTJ : public LLModel {
@@ -32,8 +31,6 @@ protected:
 
 private:
     GPTJPrivate *d_ptr;
-    std::unique_ptr<bpecpp::AdditionalVocabAdapter> m_tokav;
-    std::unique_ptr<bpecpp::BPE> m_bpe;
 };
 
 #endif // GPTJ_H

gpt4all-backend/mpt.cpp

@@ -7,7 +7,6 @@
 #include <cmath>
 #include <cstdio>
 #include <cstring>
-#include <filesystem>
 #include <fstream>
 #include <map>
 #include <random>
@@ -786,12 +785,6 @@ bool MPT::loadModel(const std::string &modelPath) {
     d_ptr->modelLoaded = true;
     d_ptr->has_im_end = d_ptr->vocab.token_to_id.find("<|im_end|>") != d_ptr->vocab.token_to_id.end();
     fflush(stdout);
-
-    if (modelPath.find("-chat") != std::string::npos) {
-        get_bpecpp_tokenizer(TokenizerType::MPT_CHAT, m_bpe, m_tokav);
-    } else {
-        get_bpecpp_tokenizer(TokenizerType::MPT, m_bpe, m_tokav);
-    }
     return true;
 }
 
@@ -847,7 +840,7 @@ void MPT::prompt(const std::string &prompt,
     int64_t t_prompt_us = 0;
 
     // tokenize the prompt
-    std::vector<uint32_t> embd_inp = m_tokav->encode(prompt, *m_bpe);
+    std::vector<int> embd_inp = gpt_tokenize(d_ptr->vocab, prompt);
 
     // save the context size
     promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
@@ -913,7 +906,6 @@ void MPT::prompt(const std::string &prompt,
     int r_instructFound = 0;
 
     std::string cachedResponse;
-    std::string decodeBuffer;
     std::vector<int> cachedTokens;
     std::unordered_set<std::string> reversePrompts
         = { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant", "### Context" };
@@ -969,7 +961,7 @@ void MPT::prompt(const std::string &prompt,
         if (id == 0 /*end of text*/)
             goto stop_generating;
 
-        const std::string str = m_tokav->decode({(uint32_t) id}, *m_bpe, true, false);
+        const std::string str = d_ptr->vocab.id_to_token[id];
 
         // Check if the provided str is part of our reverse prompts
         bool foundPartialReversePrompt = false;
@@ -999,8 +991,7 @@ void MPT::prompt(const std::string &prompt,
             if (promptCtx.tokens.size() == promptCtx.n_ctx)
                 promptCtx.tokens.erase(promptCtx.tokens.begin());
             promptCtx.tokens.push_back(t);
-            const std::string decoded = m_tokav->decode({(uint32_t) t}, *m_bpe, true, false);
+            if (!responseCallback(t, d_ptr->vocab.id_to_token[t]))
-            if (!responseCallback(t, decoded))
                 goto stop_generating;
         }
         cachedTokens.clear();

gpt4all-backend/mpt.h

@@ -5,7 +5,6 @@
 #include <functional>
 #include <vector>
 #include "llmodel.h"
-#include "tokenizer/bpe.h"
 
 class MPTPrivate;
 class MPT : public LLModel {
@@ -32,8 +31,6 @@ protected:
 
 private:
     MPTPrivate *d_ptr;
-    std::unique_ptr<bpecpp::AdditionalVocabAdapter> m_tokav;
-    std::unique_ptr<bpecpp::BPE> m_bpe;
 };
 
 #endif // MPT_H

gpt4all-backend/scripts/gen_tokenizer_include.py

@@ -1,136 +0,0 @@
-import sys
-import json
-from dataclasses import dataclass
-
-def iter_with_last(lst):
-    llen = len(lst)
-    for i, entry in enumerate(lst):
-        last = i == (llen - 1)
-        yield last, entry
-
-@dataclass
-class BufSlice:
-    offset: int
-    length: int
-    def __repr__(self):
-        return '{'f'0x{self.offset:x},{self.length}''}'
-
-def c_str_dump(bs):
-    s = bytearray()
-    s += b'"'
-    llen = 0
-    lasthex = False
-    for byte in bs:
-        if byte in (b' 01234567890abcdefghijklmnopqrstuvwxyz_-=/;:<>'
-                    b'ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*(),.[]{}`~|'):
-            # need to avoid hex characters not part of a hex escape
-            # appearing directly after a hex scape
-            if lasthex and byte in b'0123456789abcdefABCDEF':
-                s += b'""'
-                llen += 2
-            s += bytes([byte])
-            llen += 1
-            lasthex = False
-        else:
-            s += f'\\x{byte:02x}'.encode('utf8')
-            llen += 4
-            lasthex = True
-        if llen >= 80:
-            llen = 0
-            s += b"\"\n\""
-    s += b'"'
-    return s.decode('utf8')
-
-class Buf:
-    def __init__(self):
-        self.buf = b''
-        self.cache = {}
-
-    def get(self, s):
-        if s in self.cache:
-            return self.cache[s]
-        offset = len(self.buf)
-        bs = s.encode('utf8')
-        exoffs = self.buf.find(bs)
-        if exoffs != -1:
-            slc = BufSlice(offset=exoffs, length=len(bs))
-            self.cache[s] = slc
-            return slc
-        return None
-
-    def insert(self, s):
-        slc = self.get(s)
-        if slc is None:
-            bs = s.encode('utf8')
-            offset = len(self.buf)
-            self.buf += bs
-            slc = BufSlice(offset=offset, length=len(bs))
-        return slc
-
-class BreakEvery:
-    def __init__(self, n):
-        self.counter = 0
-        self.n = n
-
-    def __repr__(self):
-        self.counter += 1
-        self.counter %= self.n
-        if self.counter == 0:
-            return '\n'
-        return ''
-
-def do_convert(tkfilename, prefix):
-    with open(tkfilename, 'rb') as tkf:
-        tokconfig = json.load(tkf)
-
-    # every string in the vocab also appears in the merges list so we can store
-    # much less data in the binary by deduplicating these references, sorting by
-    # length descending makes it more likely prefixes of longer strings get
-    # deduped, and secondarily sorting lexicographically them makes the buffer
-    # data more compressible (they are not compressed in the binary itself, but
-    # the binary will be more compressible)
-    split_merges = [s.split(' ') for s in tokconfig['model']['merges']]
-    len_then = lambda m: (len(m),m)
-    avwords = sorted((av['content'] for av in tokconfig['added_tokens']), key=len_then, reverse=True)
-    all_strs = avwords + sorted(list(tokconfig['model']['vocab'].keys()), key=len_then, reverse=True)
-    buf = Buf()
-    for s in all_strs:
-        buf.insert(s)
-
-    print('// @generated GENERATED BY scripts/gen_tokenizer_include.py DO NOT MODIFY')
-    print(f'#ifndef {prefix.upper()}_TOKENIZER_CONFIG_H_')
-    print(f'#define {prefix.upper()}_TOKENIZER_CONFIG_H_')
-    print('#include "bpe.h"')
-    print(f"// buflen {len(buf.buf)}")
-    print(f"constexpr const char {prefix}_buffer[] =\n{c_str_dump(buf.buf)};")
-    avilen = len(tokconfig['added_tokens'])
-    print(f'constexpr std::array<bpecpp::additional_vocab_item_embedded, {avilen}> {prefix}_additional_vocab = ''{{')
-    for last, avi in iter_with_last(tokconfig['added_tokens']):
-        comma = ',' if not last else '' 
-        print('  {'f'.id = {avi["id"]}, .content={buf.get(avi["content"])}, .special={json.dumps(avi["special"])}''}' + comma) 
-    print('}};')
-    print()
-    mergeslen = len(tokconfig['model']['merges'])
-    print(f'constexpr std::array<std::pair<bpecpp::buf_ref, bpecpp::buf_ref>, {mergeslen}> {prefix}_merges = ''{{')
-    breaker = BreakEvery(4)
-    for last, (ma, mb) in iter_with_last(split_merges):
-        comma = ',' if not last else '' 
-        print('  {'f'{buf.get(ma)},{buf.get(mb)}''}' + comma + repr(breaker), end='')
-    print('\n}};')
-    vocablen = len(tokconfig['model']['vocab'])
-    print(f'constexpr std::array<bpecpp::buf_ref, {vocablen}> {prefix}_vocab = ''{{')
-    breaker = BreakEvery(8)
-    for last, vi in iter_with_last(tokconfig['model']['vocab']):
-        comma = ',' if not last else '' 
-        print(f'  {buf.get(vi)}' + comma + repr(breaker), end='')
-    print('\n}};')
-    print(f'#endif // {prefix.upper()}_TOKENIZER_CONFIG_H_')
-
-def main():
-    if len(sys.argv) < 3:
-        print(f'Usage: {sys.argv[0]} <hf tokenizer json> <symbol prefix>')
-        sys.exit(1)
-    do_convert(sys.argv[1], sys.argv[2])
-
-if __name__ == '__main__':
-    main()

gpt4all-backend/tokenizer/bpe.cpp

@@ -1,257 +0,0 @@
-#include "bpe.h"
-#include <unicode/normalizer2.h>
-#include <unicode/regex.h>
-#include <unicode/schriter.h>
-#include <unicode/unistr.h>
-
-#include <regex>
-#include <stdexcept>
-#include <iostream>
-
-namespace bpecpp {
-const std::string_view BPE_PRETOK_REGEX =
-    R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
-
-static void get_bigrams(const std::vector<icu::UnicodeString>& input,
-                        std::unordered_set<UnicodeBigram, bigram_hash>& pairs) {
-    pairs.clear();
-    auto i = input.begin();
-    auto prev = *i++;
-    for (; i != input.end(); ++i) {
-        pairs.insert({prev, *i});
-        prev = *i;
-    }
-}
-
-BPE::BPE(const std::unordered_map<std::string_view, uint32_t>& vocab,
-         const std::vector<std::pair<std::string_view, std::string_view>>& merges) {
-    for (auto pair : vocab) {
-        icu::UnicodeString encd = icu::UnicodeString::fromUTF8(pair.first);
-        m_vocab[encd] = pair.second;
-        m_reverse_vocab[pair.second] = encd;
-    }
-    size_t n = 0;
-    for (auto merge : merges) {
-        auto left = icu::UnicodeString::fromUTF8(merge.first);
-        auto right = icu::UnicodeString::fromUTF8(merge.second);
-        m_merges[{left, right}] = n++;
-    }
-}
-
-std::vector<uint32_t> BPE::encode(const std::string& input) {
-    auto normalized = normalize_nfc(input);
-    auto pretokenized = pretokenize(normalized);
-    std::vector<icu::UnicodeString> tokens_merged;
-    for (auto &ptok : pretokenized) {
-        bpe(ptok, tokens_merged);
-    }
-    std::vector<uint32_t> final_tokens;
-    for (auto &mtok : tokens_merged) {
-        final_tokens.push_back(m_vocab[mtok]);
-    }
-    return final_tokens;
-}
-
-std::string BPE::decode(const std::vector<uint32_t>& tokens, bool valid_utf8) {
-    std::string out;
-    for (uint32_t t : tokens) {
-        icu::UnicodeString benc = m_reverse_vocab[t];
-        icu::StringCharacterIterator schriter(benc);
-        for (UChar32 c = schriter.first32(); schriter.hasNext();
-             c = schriter.next32()) {
-            out.push_back(m_bs_table.codepoint_to_byte((uint32_t)c));
-        }
-    }
-    // roundtrip through ICU to replace invalid utf8 with U+FFFD
-    if (valid_utf8) {
-        auto tmp = icu::UnicodeString::fromUTF8(out);
-        out.clear();
-        tmp.toUTF8String(out);
-    }
-    return out;
-}
-
-// https://github.com/karpathy/minGPT/blob/37baab71b9abea1b76ab957409a1cc2fbfba8a26/mingpt/bpe.py#L95
-void BPE::bpe(icu::UnicodeString token_pretoked,
-              std::vector<icu::UnicodeString>& output) {
-    if (token_pretoked.length() < 2) {
-        output.push_back(token_pretoked);
-        return;
-    }
-    std::vector<icu::UnicodeString> words;
-    std::vector<icu::UnicodeString> words_update;
-    icu::StringCharacterIterator schriter(token_pretoked);
-    UChar32 c;
-    for (schriter.setToStart(); schriter.hasNext();) {
-        c = schriter.next32PostInc();
-        icu::UnicodeString w;
-        w.append(c);
-        words.push_back(w);
-    }
-    std::unordered_set<UnicodeBigram, bigram_hash> pairs;
-    get_bigrams(words, pairs);
-    while (true) {
-        size_t min_rank = SIZE_MAX;
-        UnicodeBigram to_merge;
-        for (auto &bigram : pairs) {
-            auto loc = m_merges.find(bigram);
-            if (loc != m_merges.end() && loc->second < min_rank) {
-                min_rank = loc->second;
-                to_merge = loc->first;
-            }
-        }
-        if (min_rank == SIZE_MAX) {
-            break;
-        } else {
-            auto i = words.begin();
-            while (i < words.end()) {
-                if (*i == to_merge.first) {
-                    auto inext = i;
-                    inext++;
-                    if (inext != words.end() && *inext == to_merge.second) {
-                        words_update.push_back(*i + *inext);
-                        i = inext;
-                    } else {
-                        words_update.push_back(*i);
-                    }
-                } else {
-                    words_update.push_back(*i);
-                }
-                ++i;
-            }
-            words.swap(words_update);
-            words_update.clear();
-            get_bigrams(words, pairs);
-        }
-    }
-    output.insert(output.end(), words.begin(), words.end());
-}
-
-std::string BPE::normalize_nfc(const std::string& input) {
-    UErrorCode uerror = U_ZERO_ERROR;
-    auto nfcnorm = icu::Normalizer2::getNFCInstance(uerror);
-    if (!U_SUCCESS(uerror))
-        throw std::runtime_error("could not get ICU NFC normalizer");
-    auto icu_ti = icu::UnicodeString::fromUTF8(input);
-    std::string out;
-    nfcnorm->normalize(icu_ti, uerror).toUTF8String(out);
-    if (!U_SUCCESS(uerror))
-        throw std::runtime_error("ICU string normalization failed");
-    return out;
-}
-
-std::vector<icu::UnicodeString> BPE::pretokenize(const std::string& input) {
-    UParseError pe;
-    UErrorCode uerror = U_ZERO_ERROR;
-    auto bpe_re_icustr = icu::UnicodeString::fromUTF8(BPE_PRETOK_REGEX);
-    if (m_pretok_re == nullptr) {
-        m_pretok_re = std::unique_ptr<icu::RegexPattern>(
-            icu::RegexPattern::compile(bpe_re_icustr, pe, uerror));
-        if (!U_SUCCESS(uerror))
-            throw std::runtime_error("Compiling BPE pretokenizer regex failed");
-    }
-    auto uinput = icu::UnicodeString::fromUTF8(input);
-    std::unique_ptr<icu::RegexMatcher> pretok_matcher(
-        m_pretok_re->matcher(uinput, uerror));
-    std::vector<icu::UnicodeString> pretoks;
-    if (!U_SUCCESS(uerror))
-        throw std::runtime_error("Creating BPE pretokenizer matcher failed");
-    while (pretok_matcher->find()) {
-        auto match = pretok_matcher->group(uerror);
-        if (!U_SUCCESS(uerror))
-            throw std::runtime_error(
-                "Getting BPE pretokenizer regex match failed");
-        std::string s;
-        icu::UnicodeString out;
-        match.toUTF8String(s);
-        for (char c : s) {
-            uint32_t codepoint = m_bs_table.byte_to_codepoint((uint8_t)c);
-            out.append((UChar32)codepoint);
-        }
-        pretoks.push_back(out);
-    }
-    return pretoks;
-}
-
-static std::string regex_escape(const std::string_view inp) {
-    std::string s(inp);
-    static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
-    return std::regex_replace(s, metacharacters, "\\$&");
-}
-
-AdditionalVocabAdapter::AdditionalVocabAdapter(
-    const std::vector<additional_vocab_item>& vocab) {
-    std::string addedtoken_regex;
-    for (const additional_vocab_item& item : vocab) {
-        if (!addedtoken_regex.empty()) {
-            addedtoken_regex += "|";
-        }
-        addedtoken_regex += regex_escape(item.content);
-        m_token_to_id[item.content] = item.id;
-        m_id_to_token[item.id] = item.content;
-        if (item.special) {
-            m_special_ids.insert(item.id);
-        }
-    }
-    m_addedtoken_re = std::regex(addedtoken_regex);
-}
-
-std::vector<uint32_t> AdditionalVocabAdapter::encode(
-    const std::string& input,
-    BPE& bpemodel,
-    bool encode_special_tokens) {
-    if (m_token_to_id.empty()) {
-        return bpemodel.encode(input);
-    }
-    std::vector<uint32_t> out;
-    std::string work = input;
-    std::smatch m;
-    while (std::regex_search(work, m, m_addedtoken_re)) {
-        auto tokloc = m_token_to_id.find(m.str());
-        if (tokloc != m_token_to_id.end()) {
-            auto tokid = tokloc->second;
-            auto prefix_decoded = bpemodel.encode(m.prefix());
-            out.insert(out.end(), prefix_decoded.begin(), prefix_decoded.end());
-            bool special = m_special_ids.find(tokid) != m_special_ids.end();
-            if (!special || encode_special_tokens) {
-                out.push_back(tokid);
-            }
-            work = m.suffix();
-        }
-    }
-    if (!work.empty()) {
-        auto rest_decoded = bpemodel.encode(work);
-        out.insert(out.end(), rest_decoded.begin(), rest_decoded.end());
-    }
-    return out;
-}
-
-std::string AdditionalVocabAdapter::decode(const std::vector<uint32_t>& tokens,
-                                           BPE& bpemodel,
-                                           bool decode_special_tokens,
-                                           bool valid_utf8) {
-    std::string out;
-    std::vector<uint32_t> to_decode;
-    for (auto tokid : tokens) {
-        auto tokloc = m_id_to_token.find(tokid);
-        if (tokloc != m_id_to_token.end()) {  // is an added token
-            if (!to_decode.empty()) {
-                out += bpemodel.decode(to_decode, valid_utf8);
-                to_decode.clear();
-            }
-            bool special = m_special_ids.find(tokid) != m_special_ids.end();
-            // only include non-special tokens unless decode_special_tokens
-            if (!special || decode_special_tokens) {
-                out += tokloc->second;
-            }
-        } else {
-            // non-added, regular token.
-            to_decode.push_back(tokid);
-        }
-    }
-    if (!to_decode.empty()) {
-        out += bpemodel.decode(to_decode, valid_utf8);
-    }
-    return out;
-}
-}  // namespace bpecpp

gpt4all-backend/tokenizer/bpe.h

@@ -1,123 +0,0 @@
-#pragma once
-#include <unicode/regex.h>
-#include <unicode/unistr.h>
-
-#include <cstdint>
-#include <regex>
-#include <unordered_map>
-#include <unordered_set>
-#include <vector>
-#include <string_view>
-
-namespace bpecpp {
-typedef std::pair<icu::UnicodeString, icu::UnicodeString> UnicodeBigram;
-
-class bpe_char_byte_table {
-   public:
-    bpe_char_byte_table() {
-        int n = 0;
-        for (uint8_t byte = 0; m_codepoint_to_byte.size() < 256; byte++) {
-            bool keep = (byte >= '!' && byte <= '~') ||
-                        (byte >= 0xa1 && byte <= 0xac) ||
-                        (byte >= 0xae && byte <= 0xff);
-            uint32_t codepoint = byte;
-            if (!keep) {
-                codepoint = 256 + n;
-                n++;
-            }
-            m_byte_to_codepoint[byte] = codepoint;
-            m_codepoint_to_byte[codepoint] = byte;
-        };
-    }
-    uint32_t byte_to_codepoint(uint8_t byte) {
-        return m_byte_to_codepoint[byte];
-    }
-
-    uint8_t codepoint_to_byte(uint32_t codepoint) {
-        return m_codepoint_to_byte.at(codepoint);
-    }
-
-   private:
-    std::array<uint32_t, 256> m_byte_to_codepoint;
-    std::unordered_map<uint32_t, uint8_t> m_codepoint_to_byte;
-};
-
-struct bigram_hash {
-    std::size_t operator()(const UnicodeBigram& pair) const {
-        return pair.first.hashCode() + pair.second.hashCode();
-    }
-};
-
-struct icu_hash {
-    std::size_t operator()(const icu::UnicodeString& us) const {
-        return us.hashCode();
-    }
-};
-
-class BPE {
-   public:
-    BPE(const std::unordered_map<std::string_view, uint32_t> &vocab,
-        const std::vector<std::pair<std::string_view, std::string_view>> &merges);
-
-    std::vector<uint32_t> encode(const std::string& input);
-
-    std::string decode(const std::vector<uint32_t>& tokens,
-                       bool valid_utf8 = true);
-
-   private:
-    std::unordered_map<icu::UnicodeString, uint32_t, icu_hash> m_vocab;
-    std::unordered_map<uint32_t, icu::UnicodeString> m_reverse_vocab;
-    std::unordered_map<UnicodeBigram, size_t, bigram_hash> m_merges;
-    bpe_char_byte_table m_bs_table;
-
-    void bpe(icu::UnicodeString token_pretoked,
-             std::vector<icu::UnicodeString>& output);
-    std::unique_ptr<icu::RegexPattern> m_pretok_re;
-    std::string normalize_nfc(const std::string& input);
-    std::vector<icu::UnicodeString> pretokenize(const std::string& input);
-};
-
-// for embedding tokenizer configs in the library - had initially constructed
-// `string_view`s in the generated headers, *but* generating thousands actual
-// references into the buffer generates thousands of *relocations* and makes
-// compilation rather slow, delaying resolving the real address into a
-// string_view until runtime fixes that
-struct buf_ref {
-    // packing these into a single u32 reduces the size of the embedded
-    // configs significantly (5.0MB->1.6MB)
-    uint32_t offset : 20;
-    uint32_t length : 12;
-
-    std::string_view into(const char* buf) {
-        return std::string_view(&buf[offset], length);
-    }
-};
-struct additional_vocab_item_embedded {
-    uint32_t id;
-    buf_ref content;
-    bool special;
-};
-struct additional_vocab_item {
-    uint32_t id;
-    std::string_view content;
-    bool special = false;
-};
-class AdditionalVocabAdapter {
-   public:
-    AdditionalVocabAdapter(const std::vector<additional_vocab_item> &vocab);
-    std::vector<uint32_t> encode(const std::string& input,
-                                 BPE& bpemodel,
-                                 bool encode_special_tokens = true);
-    std::string decode(const std::vector<uint32_t>& tokens,
-                       BPE& bpemodel,
-                       bool decode_special_tokens = true,
-                       bool valid_utf8 = true);
-
-   private:
-    std::unordered_map<std::string_view, uint32_t> m_token_to_id;
-    std::unordered_map<uint32_t, std::string_view> m_id_to_token;
-    std::unordered_set<uint32_t> m_special_ids;
-    std::regex m_addedtoken_re;
-};
-
-}  // namespace bpecpp

gpt4all-backend/utils.cpp

@@ -1,49 +1,220 @@
 #include "utils.h"
-#include "tokenizer/bpe.h"
-#include "tokenizer/mpt_tokenizer_config.h"
-#include "tokenizer/gptj_tokenizer_config.h"
 
 #include <fstream>
 #include <regex>
-#include <stdexcept>
 
-void get_bpecpp_tokenizer(const TokenizerType ttype, std::unique_ptr<bpecpp::BPE>& bpe, std::unique_ptr<bpecpp::AdditionalVocabAdapter>& av) {
+void replace(std::string & str, const std::string & needle, const std::string & replacement) {
-    std::vector<bpecpp::additional_vocab_item> avis;
+    size_t pos = 0;
-    std::unordered_map<std::string_view, uint32_t> vocab;
+    while ((pos = str.find(needle, pos)) != std::string::npos) {
-    std::vector<std::pair<std::string_view, std::string_view>> merges;
+        str.replace(pos, needle.length(), replacement);
-
+        pos += replacement.length();
-    uint32_t tok_id = 0;
-    switch (ttype) {
-        case TokenizerType::MPT_CHAT:
-            avis.push_back({ .id = 50277, .content = std::string_view("<|im_start|>"), .special = true });
-            avis.push_back({ .id = 50278, .content = std::string_view("<|im_end|>"), .special = true });
-        case TokenizerType::MPT:
-            for (auto avi_e: mpt_additional_vocab) {
-                avis.push_back({avi_e.id, avi_e.content.into(mpt_buffer), avi_e.special});
-            }
-            for (auto merge: mpt_merges) {
-                merges.push_back({merge.first.into(mpt_buffer), merge.second.into(mpt_buffer)});
-            }
-            for (auto bufref: mpt_vocab) {
-                vocab.insert({bufref.into(mpt_buffer), tok_id++});
-            }
-        break;
-        case TokenizerType::GPTJ:
-            for (auto avi_e: gptj_additional_vocab) {
-                avis.push_back({avi_e.id, avi_e.content.into(gptj_buffer), avi_e.special});
-            }
-            for (auto merge: gptj_merges) {
-                merges.push_back({merge.first.into(gptj_buffer), merge.second.into(gptj_buffer)});
-            }
-            for (auto bufref: gptj_vocab) {
-                vocab.insert({bufref.into(gptj_buffer), tok_id++});
-            }
-        break;
-        default:
-            throw std::invalid_argument("invalid tokenizer type");
     }
-    av = std::make_unique<bpecpp::AdditionalVocabAdapter>(avis);
+}
-    bpe = std::make_unique<bpecpp::BPE>(vocab, merges);
+
+std::map<std::string, int32_t> json_parse(const std::string & fname) {
+    std::map<std::string, int32_t> result;
+
+    // read file into string
+    std::string json;
+    {
+        std::ifstream ifs(fname);
+        if (!ifs) {
+            fprintf(stderr, "Failed to open %s\n", fname.c_str());
+            exit(1);
+        }
+
+        json = std::string((std::istreambuf_iterator<char>(ifs)),
+                (std::istreambuf_iterator<char>()));
+    }
+
+    if (json[0] != '{') {
+        return result;
+    }
+
+    // parse json
+    {
+        bool has_key  = false;
+        bool in_token = false;
+
+        std::string str_key = "";
+        std::string str_val = "";
+
+        int n = json.size();
+        for (int i = 1; i < n; ++i) {
+            if (!in_token) {
+                if (json[i] == ' ') continue;
+                if (json[i] == '"') {
+                    in_token = true;
+                    continue;
+                }
+            } else {
+                if (json[i] == '\\' && i+1 < n) {
+                    if (has_key == false) {
+                        str_key += json[i];
+                    } else {
+                        str_val += json[i];
+                    }
+                    ++i;
+                } else if (json[i] == '"') {
+                    if (has_key == false) {
+                        has_key = true;
+                        ++i;
+                        while (json[i] == ' ') ++i;
+                        ++i; // :
+                        while (json[i] == ' ') ++i;
+                        if (json[i] != '\"') {
+                            while (json[i] != ',' && json[i] != '}') {
+                                str_val += json[i++];
+                            }
+                            has_key = false;
+                        } else {
+                            in_token = true;
+                            continue;
+                        }
+                    } else {
+                        has_key = false;
+                    }
+
+                    ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
+                    ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
+                    ::replace(str_key, "\\\"",    "\""); // \\\"   -> "
+
+                    try {
+                        result[str_key] = std::stoi(str_val);
+                    } catch (...) {
+                        //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
+
+                    }
+                    str_key = "";
+                    str_val = "";
+                    in_token = false;
+                    continue;
+                }
+                if (has_key == false) {
+                    str_key += json[i];
+                } else {
+                    str_val += json[i];
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+std::vector<gpt_vocab::id> gpt_tokenize_inner(const gpt_vocab & vocab, const std::string & text) {
+    std::vector<std::string> words;
+
+    // first split the text into words
+    {
+        std::string str = text;
+        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+
+        std::regex re(pat);
+        std::smatch m;
+
+        while (std::regex_search(str, m, re)) {
+            for (auto x : m) {
+                words.push_back(x);
+            }
+            str = m.suffix();
+        }
+    }
+
+    // find the longest tokens that form the words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        if (word.size() == 0) continue;
+
+        int i = 0;
+        int n = word.size();
+        while (i < n) {
+            int j = n;
+            while (j > i) {
+                auto it = vocab.token_to_id.find(word.substr(i, j-i));
+                if (it != vocab.token_to_id.end()) {
+                    tokens.push_back(it->second);
+                    i = j;
+                    break;
+                }
+                --j;
+            }
+            if (i == n) {
+                break;
+            }
+            if (j == i) {
+                auto sub = word.substr(i, 1);
+                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
+                    tokens.push_back(vocab.token_to_id.at(sub));
+                } else {
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
+                }
+                ++i;
+            }
+        }
+    }
+
+    return tokens;
+}
+
+std::string regex_escape(const std::string &s) {
+  static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
+  return std::regex_replace(s, metacharacters, "\\$&");
+}
+
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
+    // Generate the subpattern from the special_tokens vector if it's not empty
+    if (!vocab.special_tokens.empty()) {
+        std::vector<gpt_vocab::id> out;
+        std::vector<std::string> chunks;
+        std::string str = text;
+        std::string special_tokens_subpattern;
+        for (const auto &token : vocab.special_tokens) {
+            if (!special_tokens_subpattern.empty()) {
+                special_tokens_subpattern += "|";
+            }
+            special_tokens_subpattern += regex_escape(token);
+        }
+        std::regex re(special_tokens_subpattern);
+        std::smatch m;
+        while (std::regex_search(str, m, re)) {
+            auto tok = vocab.token_to_id.find(m.str());
+            if (tok != vocab.token_to_id.end()) {
+                auto tokid = tok->second;
+                auto pfxtoks = gpt_tokenize_inner(vocab, m.prefix());
+                out.insert(out.end(), pfxtoks.begin(), pfxtoks.end());
+                out.push_back(tokid);
+                str = m.suffix();
+            }
+        }
+        if (!str.empty()) {
+            auto tokrest = gpt_tokenize_inner(vocab, str);
+            out.insert(out.end(), tokrest.begin(), tokrest.end());
+        }
+        return out;
+    } else {
+        return gpt_tokenize_inner(vocab, text);
+    }
+}
+
+
+bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
+    printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
+
+    vocab.token_to_id = ::json_parse(fname);
+
+    for (const auto & kv : vocab.token_to_id) {
+        vocab.id_to_token[kv.second] = kv.first;
+    }
+
+    printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
+
+    // print the vocabulary
+    //for (auto kv : vocab.token_to_id) {
+    //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
+    //}
+
+    return true;
 }
 
 gpt_vocab::id gpt_sample_top_k_top_p(

gpt4all-backend/utils.h

@@ -7,7 +7,6 @@
 #include <vector>
 #include <random>
 #include <thread>
-#include "tokenizer/bpe.h"
 
 //
 // CLI argument parsing
@@ -52,6 +51,26 @@ struct gpt_vocab {
     }
 };
 
+void replace(std::string & str, const std::string & needle, const std::string & replacement);
+
+// poor-man's JSON parsing
+std::map<std::string, int32_t> json_parse(const std::string & fname);
+
+// split text into tokens
+//
+// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
+//
+// Regex (Python):
+// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+//
+// Regex (C++):
+// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
+//
+std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
+
+// load the tokens from encoder.json
+bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
+
 // sample next token given probabilities for each embedding
 //
 //   - consider only the top K tokens
@@ -70,9 +89,3 @@ gpt_vocab::id gpt_sample_top_k_top_p(
         double temp,
         float repeat_penalty,
         std::mt19937 & rng);
-
-enum TokenizerType {
-    MPT, MPT_CHAT, GPTJ 
-};
-
-void get_bpecpp_tokenizer(const TokenizerType ttype, std::unique_ptr<bpecpp::BPE>& bpe, std::unique_ptr<bpecpp::AdditionalVocabAdapter>& av);