convert : use bytes_to_unicode from transformers
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3 changed files with 6 additions and 68 deletions
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@ -14,34 +14,13 @@ from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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def bytes_to_unicode():
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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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[:]
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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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bs.append(b)
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cs.append(2**8+n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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@ -155,7 +134,7 @@ vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_encoder = tokenization_gpt2.bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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for i in range(vocab_size):
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@ -14,6 +14,7 @@ from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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@ -22,27 +23,6 @@ import gguf
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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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[:]
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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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bs.append(b)
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cs.append(2**8+n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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@ -150,7 +130,7 @@ vocab_size = len(tokenizer_json["model"]["vocab"])
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_encoder = tokenization_gpt2.bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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for i in range(vocab_size):
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@ -14,34 +14,13 @@ from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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def bytes_to_unicode():
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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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[:]
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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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bs.append(b)
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cs.append(2**8+n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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@ -139,7 +118,7 @@ vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_encoder = tokenization_gpt2.bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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for i in range(vocab_size):
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