ExternalVendorCode/whisper.cpp
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py : make convert-pt-to-ggml.py backwards compatible with older vocab.json tokenizer files (#1001)

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* patch checkpoint convert script to keep compatibility with older hf_transformers whisper tokenizer

* typo fix
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Akash Mahajan 2023-06-25 03:50:14 -07:00 committed by GitHub
parent a7f822ef59
commit 3ec7bfffe0
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1 changed files with 28 additions and 7 deletions
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35
models/convert-pt-to-ggml.py
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@ -224,16 +224,39 @@ with np.load(dir_whisper / "whisper" / "assets" / "mel_filters.npz") as f:
#code.interact(local=locals()) #code.interact(local=locals())
# load tokenizer
# for backwards compatibility, also check for older hf_transformers format tokenizer files
# old format: dir_whisper/whisper/assets/[multilingual/gpt2]/vocab.json
# new format: dir_whisper/whisper/assets/[multilingual/gpt2].tiktoken
multilingual = hparams["n_vocab"] == 51865 multilingual = hparams["n_vocab"] == 51865
tokenizer = dir_whisper / "whisper" / "assets" / (multilingual and "multilingual.tiktoken" or "gpt2.tiktoken") tokenizer = dir_whisper / "whisper" / "assets" / (multilingual and "multilingual.tiktoken" or "gpt2.tiktoken")
tokenizer_type = "tiktoken"
if not tokenizer.is_file():
tokenizer = dir_whisper / "whisper" / "assets" / (multilingual and "multilingual" or "gpt2") / "vocab.json"
tokenizer_type = "hf_transformers"
if not tokenizer.is_file():
print("Error: failed to find either tiktoken or hf_transformers tokenizer file:", tokenizer)
sys.exit(1)
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
if tokenizer_type == "tiktoken":
with open(tokenizer, "rb") as f:
contents = f.read()
tokens = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)}
elif tokenizer_type == "hf_transformers":
with open(tokenizer, "r", encoding="utf8") as f:
_tokens_raw = json.load(f)
if '<|endoftext|>' in _tokens_raw:
# ensures exact same model as tokenizer_type == tiktoken
# details: https://github.com/ggerganov/whisper.cpp/pull/725
del _tokens_raw['<|endoftext|>']
tokens = {bytes([byte_decoder[c] for c in token]): int(idx) for token, idx in _tokens_raw.items()}
# output in the same directory as the model # output in the same directory as the model
fname_out = dir_out / "ggml-model.bin" fname_out = dir_out / "ggml-model.bin"
with open(tokenizer, "rb") as f:
contents = f.read()
tokens = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)}
# use 16-bit or 32-bit floats # use 16-bit or 32-bit floats
use_f16 = True use_f16 = True
if len(sys.argv) > 4: if len(sys.argv) > 4:
@ -262,9 +285,7 @@ for i in range(filters.shape[0]):
for j in range(filters.shape[1]): for j in range(filters.shape[1]):
fout.write(struct.pack("f", filters[i][j])) fout.write(struct.pack("f", filters[i][j]))
byte_encoder = bytes_to_unicode() # write tokenizer
byte_decoder = {v:k for k, v in byte_encoder.items()}
fout.write(struct.pack("i", len(tokens))) fout.write(struct.pack("i", len(tokens)))
for key in tokens: for key in tokens: