"""
bpe is short for Byte Pair Encoder. It translates arbitrary utf-8 strings into
sequences of integers, where each integer represents small chunks of commonly
occuring characters. This implementation is based on openai's gpt2 encoder.py:
https://github.com/openai/gpt-2/blob/master/src/encoder.py
but was mildly modified because the original implementation is a bit confusing.
I also tried to add as many comments as possible, my own understanding of what's
going on.
"""
import os
import json
import regex as re
import requests
import torch
# -----------------------------------------------------------------------------
def bytes_to_unicode():
"""
Every possible byte (really an integer 0..255) gets mapped by OpenAI to a unicode
character that represents it visually. Some bytes have their appearance preserved
because they don't cause any trouble. These are defined in list bs. For example:
chr(33) returns "!", so in the returned dictionary we simply have d[33] -> "!".
However, chr(0), for example, is '\x00', which looks ugly. So OpenAI maps these
bytes, into new characters in a range where chr() returns a single nice character.
So in the final dictionary we have d[0] -> 'Ā' instead, which is just chr(0 + 2**8).
In particular, the space character is 32, which we can see by ord(' '). Instead,
this function will shift space (32) by 256 to 288, so d[32] -> 'Ġ'.
So this is just a simple one-to-one mapping of bytes 0..255 into unicode characters
that "look nice", either in their original form, or a funny shifted character
like 'Ā', or 'Ġ', etc.
"""
# the 188 integers that render fine in their original form and need no shifting
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:] # all integers b in bs will simply map to chr(b) in the output dict
# now get the representations of the other 68 integers that do need shifting
# each will get mapped chr(256 + n), where n will grow from 0...67 in the loop
n = 0
for b in range(2**8):
if b not in bs:
# if this byte is "ugly" then map it to the next available "nice" character
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
d = dict(zip(bs, cs))
return d
def get_pairs(word):
"""
Return all bigrams as a set of tuples, of consecutive elements in the iterable word.
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class Encoder:
def __init__(self, encoder, bpe_merges):
# byte encoder/decoder
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
# bpe token encoder/decoder
self.encoder = encoder
self.decoder = {v:k for k,v in self.encoder.items()}
# bpe merge list that defines the bpe "tree", of tuples (a,b) that are to merge to token ab
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
# the splitting pattern used for pre-tokenization
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions <-- original openai comment
"""
ok so what is this regex looking for, exactly?
python re reference: https://docs.python.org/3/library/re.html
- the vertical bars | is OR, so re.findall will chunkate text as the pieces match, from left to right
- '\'s' would split up things like Andrej's -> (Andrej, 's)
- ' ?\p{L}': optional space followed by 1+ unicode code points in the category "letter"
- ' ?\p{N}': optional space followed by 1+ unicode code points in the category "number"
- ' ?[^\s\p{L}\p{N}]+': optional space, then 1+ things that are NOT a whitespace, letter or number
- '\s+(?!\S)': 1+ whitespace characters (e.g. space or tab or etc) UNLESS they are followed by non-whitespace
so this will consume whitespace characters in a sequence but exclude the last whitespace in
that sequence. that last whitespace has the opportunity to then match the optional ' ?' in
earlier patterns.
- '\s+': 1+ whitespace characters, intended probably to catch a full trailing sequence of whitespaces at end of string
So TLDR:
- we are special casing a few common apostrophe constructs ('s, 't, 're, ...) and making those into separate tokens
- we then separate out strings into consecutive chunks of 1) letters, 2) numbers, 3) non-letter-numbers, 4) whitespaces
"""
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.cache = {}
def bpe(self, token):
"""
this function uses self.bpe_ranks to iteratively merge all the possible bpe tokens
up the tree. token is a string of one individual 'word' (after regex tokenization)
and after byte encoding, e.g. 'Ġthere'.
"""
# token is a string of one individual 'word', after byte encoding, e.g. 'Ġthere'
# memoization, for efficiency
if token in self.cache:
return self.cache[token]
word = tuple(token) # individual characters that make up the token, in a tuple
pairs = get_pairs(word) # get all bigrams
if not pairs:
return token
while True:
# find the next lowest rank bigram that can be merged
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break # no more bigrams are eligible to be merged
first, second = bigram
# we will now replace all occurences of (first, second) in the list of current
# words into one merged token first_second, in the output list new_words
new_word = []
i = 0
while i < len(word):
# find the next occurence of first in the sequence of current words
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
# if this occurence is also followed by second, then merge them into one
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
# all occurences of (first, second) have been merged to first_second
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
# concat all words into a string, and use ' ' as the separator. Note that
# by now all characters have been byte encoded, guaranteeing that ' ' is
# not used in the actual data and is a 'special' delimiter character
word = ' '.join(word)
# cache the result and return
self.cache[token] = word
return word
def encode(self, text):
""" string goes in, list of integers comes out """
bpe_idx = []
# pre-tokenize the input text into string tokens (words, roughly speaking)
tokens = re.findall(self.pat, text)
# process each token into BPE integers
for token in tokens:
# encode the token as a bytes (b'') object
token_bytes = token.encode('utf-8')
# translate all bytes to their unicode string representation and flatten
token_translated = ''.join(self.byte_encoder[b] for b in token_bytes)
# perform all the applicable bpe merges according to self.bpe_ranks
token_merged = self.bpe(token_translated).split(' ')
# translate all bpe tokens to integers
token_ix = [self.encoder[bpe_token] for bpe_token in token_merged]
# extend our running list of all output integers
bpe_idx.extend(token_ix)
return bpe_idx
def encode_and_show_work(self, text):
""" debugging function, same as encode but returns all intermediate work """
bpe_idx = []
parts = []
tokens = re.findall(self.pat, text)
for token in tokens:
token_bytes = token.encode('utf-8')
token_translated = ''.join(self.byte_encoder[b] for b in token_bytes)
token_merged = self.bpe(token_translated).split(' ')
token_ix = [self.encoder[bpe_token] for bpe_token in token_merged]
bpe_idx.extend(token_ix)
parts.append({
'token': token,
'token_bytes': token_bytes,
'token_translated': token_translated,
'token_merged': token_merged,
'token_ix': token_ix,
})
out = {
'bpe_idx': bpe_idx, # the actual output sequence
'tokens': tokens, # result of pre-tokenization
'parts': parts, # intermediates for each token part
}
return out
def decode(self, bpe_idx):
""" list of integers comes in, string comes out """
# inverse map the integers to get the tokens
tokens_merged = [self.decoder[token] for token in bpe_idx]
# inverse the byte encoder, e.g. recovering 'Ġ' -> ' ', and get the bytes
tokens_flat = ''.join(tokens_merged)
tokens_bytes = bytearray([self.byte_decoder[c] for c in tokens_flat])
# recover the full utf-8 string
text = tokens_bytes.decode('utf-8', errors='replace')
return text
def get_file(local_file, remote_file):
""" downloads remote_file to local_file if necessary """
if not os.path.isfile(local_file):
print(f"downloading {remote_file} to {local_file}")
response = requests.get(remote_file)
open(local_file, "wb").write(response.content)
def get_encoder():
"""
Returns an instance of the GPT BPE Encoder/Decoder
and handles caching of "database" files.
"""
home_dir = os.path.expanduser('~')
cache_dir = os.path.join(home_dir, '.cache', 'mingpt')
os.makedirs(cache_dir, exist_ok=True)
# load encoder.json that has the raw mappings from token -> bpe index
encoder_local_file = os.path.join(cache_dir, 'encoder.json')
encoder_remote_file = 'https://openaipublic.blob.core.windows.net/gpt-2/models/124M/encoder.json'
get_file(encoder_local_file, encoder_remote_file)
with open(encoder_local_file, 'r') as f:
encoder = json.load(f)
assert len(encoder) == 50257 # 256 individual byte tokens, 50,000 merged tokens, and 1 special <|endoftext|> token
# load vocab.bpe that contains the bpe merges, i.e. the bpe tree structure
# in the form tuples (a, b), that indicate that (a, b) is to be merged to one token ab
vocab_local_file = os.path.join(cache_dir, 'vocab.bpe')
vocab_remote_file = 'https://openaipublic.blob.core.windows.net/gpt-2/models/124M/vocab.bpe'
get_file(vocab_local_file, vocab_remote_file)
with open(vocab_local_file, 'r', encoding="utf-8") as f:
bpe_data = f.read()
# light postprocessing: strip the version on first line and the last line is a blank
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
assert len(bpe_merges) == 50000 # 50,000 merged tokens
# construct the Encoder object and return
enc = Encoder(encoder, bpe_merges)
return enc
# -----------------------------------------------------------------------------
class BPETokenizer:
""" PyTorch-aware class that wraps the Encoder above """
def __init__(self):
self.encoder = get_encoder()
def __call__(self, text, return_tensors='pt'):
# PyTorch only; here because we want to match huggingface/transformers interface
assert return_tensors == 'pt'
# single string input for now, in the future potentially a list of strings
assert isinstance(text, str)
# encode and create a "batch dimension" of 1
idx = [self.encoder.encode(text)]
# wrap into PyTorch tensor
out = torch.tensor(idx, dtype=torch.long)
return out
def decode(self, idx):
# ensure a simple 1D tensor for now
assert idx.ndim == 1
# decode indices to text
text = self.encoder.decode(idx.tolist())
return text
if __name__ == '__main__':
# here is an encoding example
text = "Hello!! I'm Andrej Karpathy. It's 2022. w00t :D 🤗"
e = get_encoder()
r = e.encode_and_show_work(text)
print("Original text is:")
print(text)
print("First the text gets pre-tokenized, broken up into chunks, the outcome is:")
print(r['tokens'])
# ['Hello', '!!', ' I', "'m", ' Andrej', ' Karpathy', '.', ' It', "'s", ' 2022', '.', ' w', '00', 't', ' :', 'D', ' 🤗']
print("Then we iterate over each chunk and process them in turn...")
for part in r['parts']:
print(part)
# {'token': 'Hello', 'token_bytes': b'Hello', 'token_translated': 'Hello', 'token_merged': ['Hello'], 'token_ix': [15496]}
# {'token': '!!', 'token_bytes': b'!!', 'token_translated': '!!', 'token_merged': ['!!'], 'token_ix': [3228]}
# {'token': ' I', 'token_bytes': b' I', 'token_translated': 'ĠI', 'token_merged': ['ĠI'], 'token_ix': [314]}
# {'token': "'m", 'token_bytes': b"'m", 'token_translated': "'m", 'token_merged': ["'m"], 'token_ix': [1101]}
# {'token': ' Andrej', 'token_bytes': b' Andrej', 'token_translated': 'ĠAndrej', 'token_merged': ['ĠAndre', 'j'], 'token_ix': [10948, 73]}
# {'token': ' Karpathy', 'token_bytes': b' Karpathy', 'token_translated': 'ĠKarpathy', 'token_merged': ['ĠK', 'arp', 'athy'], 'token_ix': [509, 5117, 10036]}
# {'token': '.', 'token_bytes': b'.', 'token_translated': '.', 'token_merged': ['.'], 'token_ix': [13]}
# {'token': ' It', 'token_bytes': b' It', 'token_translated': 'ĠIt', 'token_merged': ['ĠIt'], 'token_ix': [632]}
# {'token': "'s", 'token_bytes': b"'s", 'token_translated': "'s", 'token_merged': ["'s"], 'token_ix': [338]}
# {'token': ' 2022', 'token_bytes': b' 2022', 'token_translated': 'Ġ2022', 'token_merged': ['Ġ2022'], 'token_ix': [33160]}
# {'token': '.', 'token_bytes': b'.', 'token_translated': '.', 'token_merged': ['.'], 'token_ix': [13]}
# {'token': ' w', 'token_bytes': b' w', 'token_translated': 'Ġw', 'token_merged': ['Ġw'], 'token_ix': [266]}
# {'token': '00', 'token_bytes': b'00', 'token_translated': '00', 'token_merged': ['00'], 'token_ix': [405]}
# {'token': 't', 'token_bytes': b't', 'token_translated': 't', 'token_merged': ['t'], 'token_ix': [83]}
# {'token': ' :', 'token_bytes': b' :', 'token_translated': 'Ġ:', 'token_merged': ['Ġ:'], 'token_ix': [1058]}
# {'token': 'D', 'token_bytes': b'D', 'token_translated': 'D', 'token_merged': ['D'], 'token_ix': [35]}
# {'token': ' 🤗', 'token_bytes': b' \xf0\x9f\xa4\x97', 'token_translated': 'ĠðŁ¤Ĺ', 'token_merged': ['ĠðŁ', '¤', 'Ĺ'], 'token_ix': [12520, 97, 245]}
# (refer to the code inside Encoder.encode for what these intermediates are)
print("and the final outcome is concatenating and flattening all the token_ix:")
print(r['bpe_idx'])
# [15496, 3228, 314, 1101, 10948, 73, 509, 5117, 10036, 13, 632, 338, 33160, 13, 266, 405, 83, 1058, 35, 12520, 97, 245]
# this would then become the integer input sequence to the transformer
print("ready to feed into a Transformer!")