Merge pull request #3398 from jepler/better-dictionary-compression
compression: Implement @ciscorn's dictionary approach
This commit is contained in:
Scott Shawcroft
GitHub
commit
750bc1e04a
@ -12,10 +12,14 @@ from __future__ import print_function
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import re
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import sys
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from math import log
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import collections
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import gettext
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import os.path
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sys.stdout.reconfigure(encoding='utf-8')
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sys.stderr.reconfigure(errors='backslashreplace')
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py = os.path.dirname(sys.argv[0])
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top = os.path.dirname(py)
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@ -100,77 +104,173 @@ def translate(translation_file, i18ns):
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translations.append((original, translation))
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return translations
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def frequent_ngrams(corpus, sz, n):
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return collections.Counter(corpus[i:i+sz] for i in range(len(corpus)-sz)).most_common(n)
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class TextSplitter:
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def __init__(self, words):
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words.sort(key=lambda x: len(x), reverse=True)
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self.words = set(words)
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self.pat = re.compile("|".join(re.escape(w) for w in words) + "|.", flags=re.DOTALL)
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def encode_ngrams(translation, ngrams):
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if len(ngrams) > 32:
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start = 0xe000
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def iter_words(self, text):
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s = []
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words = self.words
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for m in self.pat.finditer(text):
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t = m.group(0)
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if t in words:
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if s:
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yield (False, "".join(s))
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s = []
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yield (True, t)
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else:
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start = 0x80
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for i, g in enumerate(ngrams):
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translation = translation.replace(g, chr(start + i))
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return translation
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s.append(t)
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if s:
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yield (False, "".join(s))
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def decode_ngrams(compressed, ngrams):
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if len(ngrams) > 32:
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start, end = 0xe000, 0xf8ff
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else:
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start, end = 0x80, 0x9f
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return "".join(ngrams[ord(c) - start] if (start <= ord(c) <= end) else c for c in compressed)
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def iter(self, text):
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for m in self.pat.finditer(text):
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yield m.group(0)
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def iter_substrings(s, minlen, maxlen):
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len_s = len(s)
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maxlen = min(len_s, maxlen)
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for n in range(minlen, maxlen + 1):
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for begin in range(0, len_s - n + 1):
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yield s[begin : begin + n]
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def compute_huffman_coding(translations, compression_filename):
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texts = [t[1] for t in translations]
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words = []
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start_unused = 0x80
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end_unused = 0xff
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max_ord = 0
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for text in texts:
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for c in text:
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ord_c = ord(c)
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max_ord = max(ord_c, max_ord)
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if 0x80 <= ord_c < 0xff:
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end_unused = min(ord_c, end_unused)
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max_words = end_unused - 0x80
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values_type = "uint16_t" if max_ord > 255 else "uint8_t"
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max_words_len = 160 if max_ord > 255 else 255
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sum_len = 0
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while True:
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# Until the dictionary is filled to capacity, use a heuristic to find
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# the best "word" (2- to 9-gram) to add to it.
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#
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# The TextSplitter allows us to avoid considering parts of the text
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# that are already covered by a previously chosen word, for example
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# if "the" is in words then not only will "the" not be considered
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# again, neither will "there" or "wither", since they have "the"
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# as substrings.
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extractor = TextSplitter(words)
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counter = collections.Counter()
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for t in texts:
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for (found, word) in extractor.iter_words(t):
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if not found:
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for substr in iter_substrings(word, minlen=2, maxlen=9):
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counter[substr] += 1
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# Score the candidates we found. This is an empirical formula only,
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# chosen for its effectiveness.
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scores = sorted(
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(
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(s, (len(s) - 1) ** log(max(occ - 2, 1)), occ)
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for (s, occ) in counter.items()
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),
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key=lambda x: x[1],
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reverse=True,
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)
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# Do we have a "word" that occurred 5 times and got a score of at least
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# 5? Horray. Pick the one with the highest score.
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word = None
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for (s, score, occ) in scores:
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if occ < 5:
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continue
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if score < 5:
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break
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word = s
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break
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# If we can successfully add it to the dictionary, do so. Otherwise,
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# we've filled the dictionary to capacity and are done.
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if not word:
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break
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if sum_len + len(word) - 2 > max_words_len:
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break
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if len(words) == max_words:
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break
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words.append(word)
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sum_len += len(word) - 2
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extractor = TextSplitter(words)
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counter = collections.Counter()
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for t in texts:
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for atom in extractor.iter(t):
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counter[atom] += 1
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cb = huffman.codebook(counter.items())
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word_start = start_unused
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word_end = word_start + len(words) - 1
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print("// # words", len(words))
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print("// words", words)
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def compute_huffman_coding(translations, qstrs, compression_filename):
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all_strings = [x[1] for x in translations]
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all_strings_concat = "".join(all_strings)
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ngrams = [i[0] for i in frequent_ngrams(all_strings_concat, 2, 32)]
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all_strings_concat = encode_ngrams(all_strings_concat, ngrams)
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counts = collections.Counter(all_strings_concat)
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cb = huffman.codebook(counts.items())
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values = []
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length_count = {}
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renumbered = 0
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last_l = None
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last_length = None
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canonical = {}
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for ch, code in sorted(cb.items(), key=lambda x: (len(x[1]), x[0])):
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values.append(ch)
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l = len(code)
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if l not in length_count:
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length_count[l] = 0
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length_count[l] += 1
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if last_l:
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renumbered <<= (l - last_l)
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canonical[ch] = '{0:0{width}b}'.format(renumbered, width=l)
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s = C_ESCAPES.get(ch, ch)
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print("//", ord(ch), s, counts[ch], canonical[ch], renumbered)
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for atom, code in sorted(cb.items(), key=lambda x: (len(x[1]), x[0])):
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values.append(atom)
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length = len(code)
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if length not in length_count:
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length_count[length] = 0
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length_count[length] += 1
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if last_length:
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renumbered <<= (length - last_length)
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canonical[atom] = '{0:0{width}b}'.format(renumbered, width=length)
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# print(f"atom={repr(atom)} code={code}", file=sys.stderr)
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if len(atom) > 1:
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o = words.index(atom) + 0x80
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s = "".join(C_ESCAPES.get(ch1, ch1) for ch1 in atom)
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else:
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s = C_ESCAPES.get(atom, atom)
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o = ord(atom)
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print("//", o, s, counter[atom], canonical[atom], renumbered)
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renumbered += 1
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last_l = l
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last_length = length
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lengths = bytearray()
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print("// length count", length_count)
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print("// bigrams", ngrams)
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for i in range(1, max(length_count) + 2):
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lengths.append(length_count.get(i, 0))
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print("// values", values, "lengths", len(lengths), lengths)
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ngramdata = [ord(ni) for i in ngrams for ni in i]
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print("// estimated total memory size", len(lengths) + 2*len(values) + 2 * len(ngramdata) + sum((len(cb[u]) + 7)//8 for u in all_strings_concat))
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print("//", values, lengths)
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values_type = "uint16_t" if max(ord(u) for u in values) > 255 else "uint8_t"
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max_translation_encoded_length = max(len(translation.encode("utf-8")) for original,translation in translations)
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values = [(atom if len(atom) == 1 else chr(0x80 + words.index(atom))) for atom in values]
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print("//", values, lengths)
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max_translation_encoded_length = max(
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len(translation.encode("utf-8")) for (original, translation) in translations)
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wends = list(len(w) - 2 for w in words)
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for i in range(1, len(wends)):
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wends[i] += wends[i - 1]
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with open(compression_filename, "w") as f:
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f.write("const uint8_t lengths[] = {{ {} }};\n".format(", ".join(map(str, lengths))))
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f.write("const {} values[] = {{ {} }};\n".format(values_type, ", ".join(str(ord(u)) for u in values)))
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f.write("#define compress_max_length_bits ({})\n".format(max_translation_encoded_length.bit_length()))
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f.write("const {} bigrams[] = {{ {} }};\n".format(values_type, ", ".join(str(u) for u in ngramdata)))
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if len(ngrams) > 32:
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bigram_start = 0xe000
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else:
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bigram_start = 0x80
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bigram_end = bigram_start + len(ngrams) - 1 # End is inclusive
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f.write("#define bigram_start {}\n".format(bigram_start))
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f.write("#define bigram_end {}\n".format(bigram_end))
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return values, lengths, ngrams
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f.write("const {} words[] = {{ {} }};\n".format(values_type, ", ".join(str(ord(c)) for w in words for c in w)))
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f.write("const uint8_t wends[] = {{ {} }};\n".format(", ".join(str(p) for p in wends)))
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f.write("#define word_start {}\n".format(word_start))
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f.write("#define word_end {}\n".format(word_end))
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return (values, lengths, words, canonical, extractor)
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def decompress(encoding_table, encoded, encoded_length_bits):
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values, lengths, ngrams = encoding_table
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(values, lengths, words, _, _) = encoding_table
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dec = []
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this_byte = 0
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this_bit = 7
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@ -218,7 +318,8 @@ def decompress(encoding_table, encoded, encoded_length_bits):
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searched_length += lengths[bit_length]
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v = values[searched_length + bits - max_code]
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v = decode_ngrams(v, ngrams)
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if v >= chr(0x80) and v < chr(0x80 + len(words)):
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v = words[ord(v) - 0x80]
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i += len(v.encode('utf-8'))
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dec.append(v)
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return ''.join(dec)
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@ -226,66 +327,32 @@ def decompress(encoding_table, encoded, encoded_length_bits):
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def compress(encoding_table, decompressed, encoded_length_bits, len_translation_encoded):
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if not isinstance(decompressed, str):
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raise TypeError()
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values, lengths, ngrams = encoding_table
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decompressed = encode_ngrams(decompressed, ngrams)
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(_, _, _, canonical, extractor) = encoding_table
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enc = bytearray(len(decompressed) * 3)
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#print(decompressed)
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#print(lengths)
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current_bit = 7
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current_byte = 0
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code = len_translation_encoded
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bits = encoded_length_bits+1
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bits = encoded_length_bits + 1
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for i in range(bits - 1, 0, -1):
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if len_translation_encoded & (1 << (i - 1)):
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enc[current_byte] |= 1 << current_bit
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if current_bit == 0:
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current_bit = 7
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#print("packed {0:0{width}b}".format(enc[current_byte], width=8))
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current_byte += 1
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else:
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current_bit -= 1
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for c in decompressed:
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#print()
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#print("char", c, values.index(c))
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start = 0
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end = lengths[0]
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bits = 1
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compressed = None
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code = 0
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while compressed is None:
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s = start
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e = end
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#print("{0:0{width}b}".format(code, width=bits))
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# Binary search!
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while e > s:
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midpoint = (s + e) // 2
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#print(s, e, midpoint)
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if values[midpoint] == c:
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compressed = code + (midpoint - start)
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#print("found {0:0{width}b}".format(compressed, width=bits))
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break
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elif c < values[midpoint]:
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e = midpoint
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else:
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s = midpoint + 1
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code += end - start
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code <<= 1
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start = end
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end += lengths[bits]
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bits += 1
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#print("next bit", bits)
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for i in range(bits - 1, 0, -1):
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if compressed & (1 << (i - 1)):
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for atom in extractor.iter(decompressed):
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for b in canonical[atom]:
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if b == "1":
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enc[current_byte] |= 1 << current_bit
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if current_bit == 0:
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current_bit = 7
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#print("packed {0:0{width}b}".format(enc[current_byte], width=8))
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current_byte += 1
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else:
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current_bit -= 1
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if current_bit != 7:
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current_byte += 1
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return enc[:current_byte]
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@ -452,7 +519,7 @@ if __name__ == "__main__":
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if args.translation:
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i18ns = sorted(i18ns)
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translations = translate(args.translation, i18ns)
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encoding_table = compute_huffman_coding(translations, qstrs, args.compression_filename)
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encoding_table = compute_huffman_coding(translations, args.compression_filename)
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print_qstr_data(encoding_table, qcfgs, qstrs, translations)
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else:
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print_qstr_enums(qstrs)
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@ -47,13 +47,22 @@ STATIC int put_utf8(char *buf, int u) {
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if(u <= 0x7f) {
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*buf = u;
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return 1;
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} else if(bigram_start <= u && u <= bigram_end) {
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int n = (u - 0x80) * 2;
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// (note that at present, entries in the bigrams table are
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// guaranteed not to represent bigrams themselves, so this adds
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} else if(word_start <= u && u <= word_end) {
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uint n = (u - word_start);
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size_t pos = 0;
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if (n > 0) {
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pos = wends[n - 1] + (n * 2);
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}
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int ret = 0;
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// note that at present, entries in the words table are
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// guaranteed not to represent words themselves, so this adds
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// at most 1 level of recursive call
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int ret = put_utf8(buf, bigrams[n]);
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return ret + put_utf8(buf + ret, bigrams[n+1]);
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for(; pos < wends[n] + (n + 1) * 2; pos++) {
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int len = put_utf8(buf, words[pos]);
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buf += len;
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ret += len;
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}
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return ret;
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} else if(u <= 0x07ff) {
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*buf++ = 0b11000000 | (u >> 6);
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*buf = 0b10000000 | (u & 0b00111111);
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@ -43,6 +43,19 @@
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// (building the huffman encoding on UTF-16 code points gave better
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// compression than building it on UTF-8 bytes)
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//
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// - code points starting at 128 (word_start) and potentially extending
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// to 255 (word_end) (but never interfering with the target
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// language's used code points) stand for dictionary entries in a
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// dictionary with size up to 256 code points. The dictionary entries
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// are computed with a heuristic based on frequent substrings of 2 to
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// 9 code points. These are called "words" but are not, grammatically
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// speaking, words. They're just spans of code points that frequently
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// occur together.
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//
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// - dictionary entries are non-overlapping, and the _ending_ index of each
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// entry is stored in an array. Since the index given is the ending
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// index, the array is called "wends".
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//
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// The "data" / "tail" construct is so that the struct's last member is a
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// "flexible array". However, the _only_ member is not permitted to be
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// a flexible member, so we have to declare the first byte as a separte
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