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convert : fix vocab size when not defined in hparams #3421

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Oct 2, 2023
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convert : fix vocab size when not defined in hparams #3421

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17 changes: 5 additions & 12 deletions convert-falcon-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,26 +134,19 @@ def parse_args() -> argparse.Namespace:

tokens: list[bytearray] = []

tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")

with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)

print("gguf: get gpt2 tokenizer vocab")

# The number of tokens in tokenizer.json can differ from the expected vocab size.
# This causes downstream issues with mismatched tensor sizes when running the inference
vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"])

# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)

# The number of tokens in tokenizer.json can differ from the expected vocab size.
# This causes downstream issues with mismatched tensor sizes when running the inference
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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Using len(tokenizer.vocab) is not ideal, we really should use vocab_size from AutoConfig, which has an architecture-specific default. Although, this whole thing is a hack to work around downstream issues, right? I would much rather just store the vocab size in the GGUF and avoid this whole padding tokens mess.

assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
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15 changes: 5 additions & 10 deletions convert-gptneox-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,24 +131,19 @@ def parse_args() -> argparse.Namespace:

tokens: list[bytearray] = []

tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")

with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)

print("gguf: get gpt2 tokenizer vocab")

vocab_size = len(tokenizer_json["model"]["vocab"])

# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)

# The number of tokens in tokenizer.json can differ from the expected vocab size.
# This causes downstream issues with mismatched tensor sizes when running the inference
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
Expand Down
17 changes: 5 additions & 12 deletions convert-starcoder-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,26 +118,19 @@ def parse_args() -> argparse.Namespace:

tokens: list[bytearray] = []

tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")

with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)

print("gguf: get gpt2 tokenizer vocab")

# The number of tokens in tokenizer.json can differ from the expected vocab size.
# This causes downstream issues with mismatched tensor sizes when running the inference
vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"])

# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)

# The number of tokens in tokenizer.json can differ from the expected vocab size.
# This causes downstream issues with mismatched tensor sizes when running the inference
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
Expand Down