convert : use bytes_to_unicode from transformers

convert-falcon-hf-to-gguf.py

@@ -14,34 +14,13 @@ from typing import Any
 import numpy as np
 import torch
 from transformers import AutoTokenizer  # type: ignore[import]
+from transformers.models.gpt2 import tokenization_gpt2  # type: ignore[import]
 
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
 
-def bytes_to_unicode():
-    # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-    """
-    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)))
-
-
 def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
@@ -155,7 +134,7 @@ vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer
 tokenizer = AutoTokenizer.from_pretrained(dir_model)
 
 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()}
 
 for i in range(vocab_size):

convert-gptneox-hf-to-gguf.py

@@ -14,6 +14,7 @@ from typing import Any
 import numpy as np
 import torch
 from transformers import AutoTokenizer  # type: ignore[import]
+from transformers.models.gpt2 import tokenization_gpt2  # type: ignore[import]
 
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
@@ -22,27 +23,6 @@ import gguf
 # 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)))
-
-
 def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
@@ -150,7 +130,7 @@ vocab_size = len(tokenizer_json["model"]["vocab"])
 tokenizer = AutoTokenizer.from_pretrained(dir_model)
 
 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()}
 
 for i in range(vocab_size):

convert-starcoder-hf-to-gguf.py

@@ -14,34 +14,13 @@ from typing import Any
 import numpy as np
 import torch
 from transformers import AutoTokenizer  # type: ignore[import]
+from transformers.models.gpt2 import tokenization_gpt2  # type: ignore[import]
 
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
 
-def bytes_to_unicode():
-    # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-    """
-    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)))
-
-
 def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
@@ -139,7 +118,7 @@ vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer
 tokenizer = AutoTokenizer.from_pretrained(dir_model)
 
 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()}
 
 for i in range(vocab_size):