mirror of https://github.com/nomic-ai/gpt4all
Revert "New tokenizer implementation for MPT and GPT-J"
This reverts commit ee3469ba6c.
pull/913/head
Adam Treat
1 year ago
parent
06434f0042
commit
4a317eeb33
@ -1,4 +1,4 @@
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[codespell]
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skip = .git,*.pdf,*.svg,*_tokenizer_config.h
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skip = .git,*.pdf,*.svg
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#
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# ignore-words-list =
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@ -1,136 +0,0 @@
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import sys
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import json
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from dataclasses import dataclass
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def iter_with_last(lst):
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llen = len(lst)
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for i, entry in enumerate(lst):
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last = i == (llen - 1)
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yield last, entry
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@dataclass
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class BufSlice:
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offset: int
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length: int
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def __repr__(self):
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return '{'f'0x{self.offset:x},{self.length}''}'
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def c_str_dump(bs):
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s = bytearray()
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s += b'"'
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llen = 0
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lasthex = False
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for byte in bs:
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if byte in (b' 01234567890abcdefghijklmnopqrstuvwxyz_-=/;:<>'
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b'ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*(),.[]{}`~|'):
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# need to avoid hex characters not part of a hex escape
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# appearing directly after a hex scape
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if lasthex and byte in b'0123456789abcdefABCDEF':
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s += b'""'
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llen += 2
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s += bytes([byte])
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llen += 1
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lasthex = False
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else:
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s += f'\\x{byte:02x}'.encode('utf8')
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llen += 4
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lasthex = True
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if llen >= 80:
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llen = 0
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s += b"\"\n\""
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s += b'"'
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return s.decode('utf8')
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class Buf:
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def __init__(self):
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self.buf = b''
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self.cache = {}
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def get(self, s):
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if s in self.cache:
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return self.cache[s]
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offset = len(self.buf)
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bs = s.encode('utf8')
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exoffs = self.buf.find(bs)
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if exoffs != -1:
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slc = BufSlice(offset=exoffs, length=len(bs))
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self.cache[s] = slc
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return slc
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return None
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def insert(self, s):
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slc = self.get(s)
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if slc is None:
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bs = s.encode('utf8')
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offset = len(self.buf)
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self.buf += bs
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slc = BufSlice(offset=offset, length=len(bs))
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return slc
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class BreakEvery:
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def __init__(self, n):
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self.counter = 0
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self.n = n
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def __repr__(self):
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self.counter += 1
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self.counter %= self.n
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if self.counter == 0:
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return '\n'
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return ''
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def do_convert(tkfilename, prefix):
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with open(tkfilename, 'rb') as tkf:
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tokconfig = json.load(tkf)
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# every string in the vocab also appears in the merges list so we can store
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# much less data in the binary by deduplicating these references, sorting by
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# length descending makes it more likely prefixes of longer strings get
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# deduped, and secondarily sorting lexicographically them makes the buffer
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# data more compressible (they are not compressed in the binary itself, but
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# the binary will be more compressible)
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split_merges = [s.split(' ') for s in tokconfig['model']['merges']]
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len_then = lambda m: (len(m),m)
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avwords = sorted((av['content'] for av in tokconfig['added_tokens']), key=len_then, reverse=True)
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all_strs = avwords + sorted(list(tokconfig['model']['vocab'].keys()), key=len_then, reverse=True)
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buf = Buf()
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for s in all_strs:
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buf.insert(s)
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print('// @generated GENERATED BY scripts/gen_tokenizer_include.py DO NOT MODIFY')
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print(f'#ifndef {prefix.upper()}_TOKENIZER_CONFIG_H_')
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print(f'#define {prefix.upper()}_TOKENIZER_CONFIG_H_')
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print('#include "bpe.h"')
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print(f"// buflen {len(buf.buf)}")
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print(f"constexpr const char {prefix}_buffer[] =\n{c_str_dump(buf.buf)};")
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avilen = len(tokconfig['added_tokens'])
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print(f'constexpr std::array<bpecpp::additional_vocab_item_embedded, {avilen}> {prefix}_additional_vocab = ''{{')
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for last, avi in iter_with_last(tokconfig['added_tokens']):
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comma = ',' if not last else ''
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print(' {'f'.id = {avi["id"]}, .content={buf.get(avi["content"])}, .special={json.dumps(avi["special"])}''}' + comma)
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print('}};')
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print()
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mergeslen = len(tokconfig['model']['merges'])
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print(f'constexpr std::array<std::pair<bpecpp::buf_ref, bpecpp::buf_ref>, {mergeslen}> {prefix}_merges = ''{{')
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breaker = BreakEvery(4)
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for last, (ma, mb) in iter_with_last(split_merges):
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comma = ',' if not last else ''
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print(' {'f'{buf.get(ma)},{buf.get(mb)}''}' + comma + repr(breaker), end='')
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print('\n}};')
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vocablen = len(tokconfig['model']['vocab'])
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print(f'constexpr std::array<bpecpp::buf_ref, {vocablen}> {prefix}_vocab = ''{{')
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breaker = BreakEvery(8)
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for last, vi in iter_with_last(tokconfig['model']['vocab']):
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comma = ',' if not last else ''
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print(f' {buf.get(vi)}' + comma + repr(breaker), end='')
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print('\n}};')
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print(f'#endif // {prefix.upper()}_TOKENIZER_CONFIG_H_')
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def main():
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if len(sys.argv) < 3:
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print(f'Usage: {sys.argv[0]} <hf tokenizer json> <symbol prefix>')
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sys.exit(1)
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do_convert(sys.argv[1], sys.argv[2])
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if __name__ == '__main__':
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main()
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@ -1,257 +0,0 @@
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#include "bpe.h"
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#include <unicode/normalizer2.h>
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#include <unicode/regex.h>
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#include <unicode/schriter.h>
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#include <unicode/unistr.h>
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#include <regex>
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#include <stdexcept>
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#include <iostream>
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namespace bpecpp {
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const std::string_view BPE_PRETOK_REGEX =
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R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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static void get_bigrams(const std::vector<icu::UnicodeString>& input,
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std::unordered_set<UnicodeBigram, bigram_hash>& pairs) {
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pairs.clear();
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auto i = input.begin();
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auto prev = *i++;
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for (; i != input.end(); ++i) {
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pairs.insert({prev, *i});
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prev = *i;
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}
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}
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BPE::BPE(const std::unordered_map<std::string_view, uint32_t>& vocab,
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const std::vector<std::pair<std::string_view, std::string_view>>& merges) {
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for (auto pair : vocab) {
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icu::UnicodeString encd = icu::UnicodeString::fromUTF8(pair.first);
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m_vocab[encd] = pair.second;
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m_reverse_vocab[pair.second] = encd;
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}
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size_t n = 0;
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for (auto merge : merges) {
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auto left = icu::UnicodeString::fromUTF8(merge.first);
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auto right = icu::UnicodeString::fromUTF8(merge.second);
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m_merges[{left, right}] = n++;
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}
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}
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std::vector<uint32_t> BPE::encode(const std::string& input) {
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auto normalized = normalize_nfc(input);
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auto pretokenized = pretokenize(normalized);
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std::vector<icu::UnicodeString> tokens_merged;
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for (auto &ptok : pretokenized) {
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bpe(ptok, tokens_merged);
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}
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std::vector<uint32_t> final_tokens;
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for (auto &mtok : tokens_merged) {
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final_tokens.push_back(m_vocab[mtok]);
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}
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return final_tokens;
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}
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std::string BPE::decode(const std::vector<uint32_t>& tokens, bool valid_utf8) {
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std::string out;
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for (uint32_t t : tokens) {
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icu::UnicodeString benc = m_reverse_vocab[t];
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icu::StringCharacterIterator schriter(benc);
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for (UChar32 c = schriter.first32(); schriter.hasNext();
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c = schriter.next32()) {
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out.push_back(m_bs_table.codepoint_to_byte((uint32_t)c));
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}
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}
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// roundtrip through ICU to replace invalid utf8 with U+FFFD
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if (valid_utf8) {
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auto tmp = icu::UnicodeString::fromUTF8(out);
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out.clear();
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tmp.toUTF8String(out);
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}
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return out;
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}
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// https://github.com/karpathy/minGPT/blob/37baab71b9abea1b76ab957409a1cc2fbfba8a26/mingpt/bpe.py#L95
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void BPE::bpe(icu::UnicodeString token_pretoked,
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std::vector<icu::UnicodeString>& output) {
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if (token_pretoked.length() < 2) {
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output.push_back(token_pretoked);
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return;
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}
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std::vector<icu::UnicodeString> words;
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std::vector<icu::UnicodeString> words_update;
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icu::StringCharacterIterator schriter(token_pretoked);
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UChar32 c;
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for (schriter.setToStart(); schriter.hasNext();) {
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c = schriter.next32PostInc();
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icu::UnicodeString w;
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w.append(c);
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words.push_back(w);
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}
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std::unordered_set<UnicodeBigram, bigram_hash> pairs;
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get_bigrams(words, pairs);
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while (true) {
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size_t min_rank = SIZE_MAX;
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UnicodeBigram to_merge;
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for (auto &bigram : pairs) {
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auto loc = m_merges.find(bigram);
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if (loc != m_merges.end() && loc->second < min_rank) {
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min_rank = loc->second;
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to_merge = loc->first;
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}
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}
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if (min_rank == SIZE_MAX) {
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break;
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} else {
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auto i = words.begin();
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while (i < words.end()) {
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if (*i == to_merge.first) {
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auto inext = i;
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inext++;
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if (inext != words.end() && *inext == to_merge.second) {
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words_update.push_back(*i + *inext);
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i = inext;
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} else {
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words_update.push_back(*i);
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}
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} else {
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words_update.push_back(*i);
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}
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++i;
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}
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words.swap(words_update);
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words_update.clear();
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get_bigrams(words, pairs);
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}
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}
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output.insert(output.end(), words.begin(), words.end());
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}
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std::string BPE::normalize_nfc(const std::string& input) {
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UErrorCode uerror = U_ZERO_ERROR;
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auto nfcnorm = icu::Normalizer2::getNFCInstance(uerror);
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if (!U_SUCCESS(uerror))
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throw std::runtime_error("could not get ICU NFC normalizer");
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auto icu_ti = icu::UnicodeString::fromUTF8(input);
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std::string out;
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nfcnorm->normalize(icu_ti, uerror).toUTF8String(out);
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if (!U_SUCCESS(uerror))
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throw std::runtime_error("ICU string normalization failed");
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return out;
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}
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std::vector<icu::UnicodeString> BPE::pretokenize(const std::string& input) {
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UParseError pe;
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UErrorCode uerror = U_ZERO_ERROR;
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auto bpe_re_icustr = icu::UnicodeString::fromUTF8(BPE_PRETOK_REGEX);
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if (m_pretok_re == nullptr) {
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m_pretok_re = std::unique_ptr<icu::RegexPattern>(
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icu::RegexPattern::compile(bpe_re_icustr, pe, uerror));
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if (!U_SUCCESS(uerror))
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throw std::runtime_error("Compiling BPE pretokenizer regex failed");
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}
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auto uinput = icu::UnicodeString::fromUTF8(input);
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std::unique_ptr<icu::RegexMatcher> pretok_matcher(
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m_pretok_re->matcher(uinput, uerror));
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std::vector<icu::UnicodeString> pretoks;
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if (!U_SUCCESS(uerror))
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throw std::runtime_error("Creating BPE pretokenizer matcher failed");
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while (pretok_matcher->find()) {
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auto match = pretok_matcher->group(uerror);
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if (!U_SUCCESS(uerror))
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throw std::runtime_error(
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"Getting BPE pretokenizer regex match failed");
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std::string s;
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icu::UnicodeString out;
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match.toUTF8String(s);
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for (char c : s) {
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uint32_t codepoint = m_bs_table.byte_to_codepoint((uint8_t)c);
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out.append((UChar32)codepoint);
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}
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pretoks.push_back(out);
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}
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return pretoks;
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}
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static std::string regex_escape(const std::string_view inp) {
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std::string s(inp);
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static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
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return std::regex_replace(s, metacharacters, "\\$&");
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}
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AdditionalVocabAdapter::AdditionalVocabAdapter(
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const std::vector<additional_vocab_item>& vocab) {
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std::string addedtoken_regex;
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for (const additional_vocab_item& item : vocab) {
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if (!addedtoken_regex.empty()) {
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addedtoken_regex += "|";
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}
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addedtoken_regex += regex_escape(item.content);
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m_token_to_id[item.content] = item.id;
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m_id_to_token[item.id] = item.content;
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if (item.special) {
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m_special_ids.insert(item.id);
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}
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}
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m_addedtoken_re = std::regex(addedtoken_regex);
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}
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std::vector<uint32_t> AdditionalVocabAdapter::encode(
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const std::string& input,
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BPE& bpemodel,
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bool encode_special_tokens) {
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if (m_token_to_id.empty()) {
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return bpemodel.encode(input);
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}
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std::vector<uint32_t> out;
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std::string work = input;
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std::smatch m;
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while (std::regex_search(work, m, m_addedtoken_re)) {
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auto tokloc = m_token_to_id.find(m.str());
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if (tokloc != m_token_to_id.end()) {
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auto tokid = tokloc->second;
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auto prefix_decoded = bpemodel.encode(m.prefix());
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out.insert(out.end(), prefix_decoded.begin(), prefix_decoded.end());
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bool special = m_special_ids.find(tokid) != m_special_ids.end();
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if (!special || encode_special_tokens) {
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out.push_back(tokid);
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}
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work = m.suffix();
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}
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}
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if (!work.empty()) {
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auto rest_decoded = bpemodel.encode(work);
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out.insert(out.end(), rest_decoded.begin(), rest_decoded.end());
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}
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return out;
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}
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std::string AdditionalVocabAdapter::decode(const std::vector<uint32_t>& tokens,
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BPE& bpemodel,
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bool decode_special_tokens,
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bool valid_utf8) {
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std::string out;
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std::vector<uint32_t> to_decode;
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for (auto tokid : tokens) {
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auto tokloc = m_id_to_token.find(tokid);
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if (tokloc != m_id_to_token.end()) { // is an added token
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if (!to_decode.empty()) {
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out += bpemodel.decode(to_decode, valid_utf8);
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to_decode.clear();
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}
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bool special = m_special_ids.find(tokid) != m_special_ids.end();
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// only include non-special tokens unless decode_special_tokens
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if (!special || decode_special_tokens) {
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out += tokloc->second;
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}
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} else {
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// non-added, regular token.
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to_decode.push_back(tokid);
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}
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}
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if (!to_decode.empty()) {
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out += bpemodel.decode(to_decode, valid_utf8);
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}
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return out;
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}
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} // namespace bpecpp
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@ -1,123 +0,0 @@
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#pragma once
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#include <unicode/regex.h>
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#include <unicode/unistr.h>
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#include <cstdint>
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#include <regex>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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#include <string_view>
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namespace bpecpp {
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typedef std::pair<icu::UnicodeString, icu::UnicodeString> UnicodeBigram;
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class bpe_char_byte_table {
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public:
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bpe_char_byte_table() {
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int n = 0;
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for (uint8_t byte = 0; m_codepoint_to_byte.size() < 256; byte++) {
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bool keep = (byte >= '!' && byte <= '~') ||
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(byte >= 0xa1 && byte <= 0xac) ||
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(byte >= 0xae && byte <= 0xff);
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uint32_t codepoint = byte;
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if (!keep) {
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codepoint = 256 + n;
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n++;
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}
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m_byte_to_codepoint[byte] = codepoint;
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m_codepoint_to_byte[codepoint] = byte;
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};
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}
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||||
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
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue