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convert scripts: use bytes_to_unicode from transformers

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Cebtenzzre 2023-09-29 17:39:49 -04:00 committed by Adam Treat
parent a49a1dcdf4
commit 0493e6eb07
2 changed files with 5 additions and 48 deletions
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25
gpt4all-backend/scripts/convert_gptj_to_gguf.py
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@ -27,28 +27,7 @@ from pathlib import Path
import gguf import gguf
import numpy as np import numpy as np
from transformers import AutoTokenizer, GPTJConfig, GPTJForCausalLM from transformers import AutoTokenizer, GPTJConfig, GPTJForCausalLM
from transformers.models.gpt2 import tokenization_gpt2
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
return dict(zip(bs, (chr(n) for n in cs)))
if not 2 <= len(sys.argv) < 4: if not 2 <= len(sys.argv) < 4:
@ -100,7 +79,7 @@ print("gguf: get gpt2 tokenizer vocab")
tokenizer = AutoTokenizer.from_pretrained(dir_model) tokenizer = AutoTokenizer.from_pretrained(dir_model)
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode() byte_encoder = tokenization_gpt2.bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()} byte_decoder = {v: k for k, v in byte_encoder.items()}
tokens: list[bytearray] = [] tokens: list[bytearray] = []

28
gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py
View File

@ -18,30 +18,8 @@ from pathlib import Path
import gguf import gguf
import numpy as np import numpy as np
import torch import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BloomForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
from transformers.models.gpt2 import tokenization_gpt2
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if not 3 <= len(sys.argv) < 5: if not 3 <= len(sys.argv) < 5:
@ -104,7 +82,7 @@ special_ids = tokenizer.all_special_ids
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
added_tokens = tokenizer.get_added_vocab().values() added_tokens = tokenizer.get_added_vocab().values()
byte_encoder = bytes_to_unicode() byte_encoder = tokenization_gpt2.bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()} byte_decoder = {v: k for k, v in byte_encoder.items()}
tokens: list[bytearray] = [] tokens: list[bytearray] = []