mirror of
https://github.com/karpathy/minGPT
synced 2024-04-25 03:15:12 +02:00
320 lines
16 KiB
Python
320 lines
16 KiB
Python
"""
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bpe is short for Byte Pair Encoder. It translates arbitrary utf-8 strings into
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sequences of integers, where each integer represents small chunks of commonly
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occuring characters. This implementation is based on openai's gpt2 encoder.py:
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https://github.com/openai/gpt-2/blob/master/src/encoder.py
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but was mildly modified because the original implementation is a bit confusing.
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I also tried to add as many comments as possible, my own understanding of what's
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going on.
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"""
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import os
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import json
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import regex as re
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import requests
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import torch
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# -----------------------------------------------------------------------------
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def bytes_to_unicode():
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"""
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Every possible byte (really an integer 0..255) gets mapped by OpenAI to a unicode
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character that represents it visually. Some bytes have their appearance preserved
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because they don't cause any trouble. These are defined in list bs. For example:
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chr(33) returns "!", so in the returned dictionary we simply have d[33] -> "!".
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However, chr(0), for example, is '\x00', which looks ugly. So OpenAI maps these
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bytes, into new characters in a range where chr() returns a single nice character.
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So in the final dictionary we have d[0] -> 'Ā' instead, which is just chr(0 + 2**8).
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In particular, the space character is 32, which we can see by ord(' '). Instead,
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this function will shift space (32) by 256 to 288, so d[32] -> 'Ġ'.
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So this is just a simple one-to-one mapping of bytes 0..255 into unicode characters
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that "look nice", either in their original form, or a funny shifted character
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like 'Ā', or 'Ġ', etc.
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"""
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# the 188 integers that render fine in their original form and need no shifting
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:] # all integers b in bs will simply map to chr(b) in the output dict
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# now get the representations of the other 68 integers that do need shifting
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# each will get mapped chr(256 + n), where n will grow from 0...67 in the loop
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n = 0
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for b in range(2**8):
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if b not in bs:
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# if this byte is "ugly" then map it to the next available "nice" character
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [chr(n) for n in cs]
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d = dict(zip(bs, cs))
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return d
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def get_pairs(word):
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"""
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Return all bigrams as a set of tuples, of consecutive elements in the iterable word.
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class Encoder:
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def __init__(self, encoder, bpe_merges):
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# byte encoder/decoder
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
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# bpe token encoder/decoder
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self.encoder = encoder
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self.decoder = {v:k for k,v in self.encoder.items()}
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# bpe merge list that defines the bpe "tree", of tuples (a,b) that are to merge to token ab
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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# the splitting pattern used for pre-tokenization
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# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions <-- original openai comment
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"""
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ok so what is this regex looking for, exactly?
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python re reference: https://docs.python.org/3/library/re.html
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- the vertical bars | is OR, so re.findall will chunkate text as the pieces match, from left to right
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- '\'s' would split up things like Andrej's -> (Andrej, 's)
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- ' ?\p{L}': optional space followed by 1+ unicode code points in the category "letter"
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- ' ?\p{N}': optional space followed by 1+ unicode code points in the category "number"
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- ' ?[^\s\p{L}\p{N}]+': optional space, then 1+ things that are NOT a whitespace, letter or number
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- '\s+(?!\S)': 1+ whitespace characters (e.g. space or tab or etc) UNLESS they are followed by non-whitespace
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so this will consume whitespace characters in a sequence but exclude the last whitespace in
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that sequence. that last whitespace has the opportunity to then match the optional ' ?' in
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earlier patterns.
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- '\s+': 1+ whitespace characters, intended probably to catch a full trailing sequence of whitespaces at end of string
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So TLDR:
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- we are special casing a few common apostrophe constructs ('s, 't, 're, ...) and making those into separate tokens
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- we then separate out strings into consecutive chunks of 1) letters, 2) numbers, 3) non-letter-numbers, 4) whitespaces
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"""
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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self.cache = {}
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def bpe(self, token):
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"""
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this function uses self.bpe_ranks to iteratively merge all the possible bpe tokens
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up the tree. token is a string of one individual 'word' (after regex tokenization)
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and after byte encoding, e.g. 'Ġthere'.
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"""
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# token is a string of one individual 'word', after byte encoding, e.g. 'Ġthere'
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# memoization, for efficiency
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if token in self.cache:
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return self.cache[token]
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word = tuple(token) # individual characters that make up the token, in a tuple
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pairs = get_pairs(word) # get all bigrams
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if not pairs:
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return token
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while True:
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# find the next lowest rank bigram that can be merged
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bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
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if bigram not in self.bpe_ranks:
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break # no more bigrams are eligible to be merged
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first, second = bigram
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# we will now replace all occurences of (first, second) in the list of current
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# words into one merged token first_second, in the output list new_words
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new_word = []
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i = 0
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while i < len(word):
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# find the next occurence of first in the sequence of current words
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try:
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j = word.index(first, i)
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new_word.extend(word[i:j])
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i = j
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except:
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new_word.extend(word[i:])
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break
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# if this occurence is also followed by second, then merge them into one
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if word[i] == first and i < len(word)-1 and word[i+1] == second:
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new_word.append(first+second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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# all occurences of (first, second) have been merged to first_second
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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# concat all words into a string, and use ' ' as the separator. Note that
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# by now all characters have been byte encoded, guaranteeing that ' ' is
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# not used in the actual data and is a 'special' delimiter character
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word = ' '.join(word)
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# cache the result and return
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self.cache[token] = word
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return word
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def encode(self, text):
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""" string goes in, list of integers comes out """
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bpe_idx = []
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# pre-tokenize the input text into string tokens (words, roughly speaking)
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tokens = re.findall(self.pat, text)
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# process each token into BPE integers
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for token in tokens:
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# encode the token as a bytes (b'') object
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token_bytes = token.encode('utf-8')
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# translate all bytes to their unicode string representation and flatten
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token_translated = ''.join(self.byte_encoder[b] for b in token_bytes)
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# perform all the applicable bpe merges according to self.bpe_ranks
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token_merged = self.bpe(token_translated).split(' ')
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# translate all bpe tokens to integers
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token_ix = [self.encoder[bpe_token] for bpe_token in token_merged]
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# extend our running list of all output integers
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bpe_idx.extend(token_ix)
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return bpe_idx
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def encode_and_show_work(self, text):
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""" debugging function, same as encode but returns all intermediate work """
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bpe_idx = []
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parts = []
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tokens = re.findall(self.pat, text)
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for token in tokens:
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token_bytes = token.encode('utf-8')
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token_translated = ''.join(self.byte_encoder[b] for b in token_bytes)
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token_merged = self.bpe(token_translated).split(' ')
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token_ix = [self.encoder[bpe_token] for bpe_token in token_merged]
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bpe_idx.extend(token_ix)
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parts.append({
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'token': token,
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'token_bytes': token_bytes,
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'token_translated': token_translated,
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'token_merged': token_merged,
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'token_ix': token_ix,
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})
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out = {
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'bpe_idx': bpe_idx, # the actual output sequence
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'tokens': tokens, # result of pre-tokenization
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'parts': parts, # intermediates for each token part
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}
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return out
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def decode(self, bpe_idx):
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""" list of integers comes in, string comes out """
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# inverse map the integers to get the tokens
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tokens_merged = [self.decoder[token] for token in bpe_idx]
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# inverse the byte encoder, e.g. recovering 'Ġ' -> ' ', and get the bytes
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tokens_flat = ''.join(tokens_merged)
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tokens_bytes = bytearray([self.byte_decoder[c] for c in tokens_flat])
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# recover the full utf-8 string
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text = tokens_bytes.decode('utf-8', errors='replace')
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return text
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def get_file(local_file, remote_file):
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""" downloads remote_file to local_file if necessary """
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if not os.path.isfile(local_file):
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print(f"downloading {remote_file} to {local_file}")
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response = requests.get(remote_file)
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open(local_file, "wb").write(response.content)
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def get_encoder():
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"""
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Returns an instance of the GPT BPE Encoder/Decoder
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and handles caching of "database" files.
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"""
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home_dir = os.path.expanduser('~')
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cache_dir = os.path.join(home_dir, '.cache', 'mingpt')
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os.makedirs(cache_dir, exist_ok=True)
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# load encoder.json that has the raw mappings from token -> bpe index
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encoder_local_file = os.path.join(cache_dir, 'encoder.json')
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encoder_remote_file = 'https://openaipublic.blob.core.windows.net/gpt-2/models/124M/encoder.json'
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get_file(encoder_local_file, encoder_remote_file)
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with open(encoder_local_file, 'r') as f:
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encoder = json.load(f)
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assert len(encoder) == 50257 # 256 individual byte tokens, 50,000 merged tokens, and 1 special <|endoftext|> token
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# load vocab.bpe that contains the bpe merges, i.e. the bpe tree structure
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# in the form tuples (a, b), that indicate that (a, b) is to be merged to one token ab
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vocab_local_file = os.path.join(cache_dir, 'vocab.bpe')
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vocab_remote_file = 'https://openaipublic.blob.core.windows.net/gpt-2/models/124M/vocab.bpe'
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get_file(vocab_local_file, vocab_remote_file)
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with open(vocab_local_file, 'r', encoding="utf-8") as f:
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bpe_data = f.read()
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# light postprocessing: strip the version on first line and the last line is a blank
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bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
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assert len(bpe_merges) == 50000 # 50,000 merged tokens
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# construct the Encoder object and return
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enc = Encoder(encoder, bpe_merges)
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return enc
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# -----------------------------------------------------------------------------
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class BPETokenizer:
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""" PyTorch-aware class that wraps the Encoder above """
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def __init__(self):
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self.encoder = get_encoder()
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def __call__(self, text, return_tensors='pt'):
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# PyTorch only; here because we want to match huggingface/transformers interface
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assert return_tensors == 'pt'
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# single string input for now, in the future potentially a list of strings
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assert isinstance(text, str)
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# encode and create a "batch dimension" of 1
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idx = [self.encoder.encode(text)]
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# wrap into PyTorch tensor
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out = torch.tensor(idx, dtype=torch.long)
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return out
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def decode(self, idx):
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# ensure a simple 1D tensor for now
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assert idx.ndim == 1
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# decode indices to text
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text = self.encoder.decode(idx.tolist())
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return text
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if __name__ == '__main__':
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# here is an encoding example
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text = "Hello!! I'm Andrej Karpathy. It's 2022. w00t :D 🤗"
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e = get_encoder()
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r = e.encode_and_show_work(text)
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print("Original text is:")
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print(text)
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print("First the text gets pre-tokenized, broken up into chunks, the outcome is:")
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print(r['tokens'])
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# ['Hello', '!!', ' I', "'m", ' Andrej', ' Karpathy', '.', ' It', "'s", ' 2022', '.', ' w', '00', 't', ' :', 'D', ' 🤗']
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print("Then we iterate over each chunk and process them in turn...")
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for part in r['parts']:
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print(part)
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# {'token': 'Hello', 'token_bytes': b'Hello', 'token_translated': 'Hello', 'token_merged': ['Hello'], 'token_ix': [15496]}
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# {'token': '!!', 'token_bytes': b'!!', 'token_translated': '!!', 'token_merged': ['!!'], 'token_ix': [3228]}
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# {'token': ' I', 'token_bytes': b' I', 'token_translated': 'ĠI', 'token_merged': ['ĠI'], 'token_ix': [314]}
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# {'token': "'m", 'token_bytes': b"'m", 'token_translated': "'m", 'token_merged': ["'m"], 'token_ix': [1101]}
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# {'token': ' Andrej', 'token_bytes': b' Andrej', 'token_translated': 'ĠAndrej', 'token_merged': ['ĠAndre', 'j'], 'token_ix': [10948, 73]}
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# {'token': ' Karpathy', 'token_bytes': b' Karpathy', 'token_translated': 'ĠKarpathy', 'token_merged': ['ĠK', 'arp', 'athy'], 'token_ix': [509, 5117, 10036]}
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# {'token': '.', 'token_bytes': b'.', 'token_translated': '.', 'token_merged': ['.'], 'token_ix': [13]}
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# {'token': ' It', 'token_bytes': b' It', 'token_translated': 'ĠIt', 'token_merged': ['ĠIt'], 'token_ix': [632]}
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# {'token': "'s", 'token_bytes': b"'s", 'token_translated': "'s", 'token_merged': ["'s"], 'token_ix': [338]}
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# {'token': ' 2022', 'token_bytes': b' 2022', 'token_translated': 'Ġ2022', 'token_merged': ['Ġ2022'], 'token_ix': [33160]}
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# {'token': '.', 'token_bytes': b'.', 'token_translated': '.', 'token_merged': ['.'], 'token_ix': [13]}
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# {'token': ' w', 'token_bytes': b' w', 'token_translated': 'Ġw', 'token_merged': ['Ġw'], 'token_ix': [266]}
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# {'token': '00', 'token_bytes': b'00', 'token_translated': '00', 'token_merged': ['00'], 'token_ix': [405]}
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# {'token': 't', 'token_bytes': b't', 'token_translated': 't', 'token_merged': ['t'], 'token_ix': [83]}
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# {'token': ' :', 'token_bytes': b' :', 'token_translated': 'Ġ:', 'token_merged': ['Ġ:'], 'token_ix': [1058]}
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# {'token': 'D', 'token_bytes': b'D', 'token_translated': 'D', 'token_merged': ['D'], 'token_ix': [35]}
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# {'token': ' 🤗', 'token_bytes': b' \xf0\x9f\xa4\x97', 'token_translated': 'ĠðŁ¤Ĺ', 'token_merged': ['ĠðŁ', '¤', 'Ĺ'], 'token_ix': [12520, 97, 245]}
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# (refer to the code inside Encoder.encode for what these intermediates are)
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print("and the final outcome is concatenating and flattening all the token_ix:")
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print(r['bpe_idx'])
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# [15496, 3228, 314, 1101, 10948, 73, 509, 5117, 10036, 13, 632, 338, 33160, 13, 266, 405, 83, 1058, 35, 12520, 97, 245]
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# this would then become the integer input sequence to the transformer
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print("ready to feed into a Transformer!")
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