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feat: add model-converter [TAB-153] (#357)
* feat: add transformers-to-ctranslate * chore: resolve comments * chore: fix * chore: remove dotenv * chore: resolve comments * chore: lint * chore: change dir name
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import argparse | ||
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def make_parser(): | ||
parser = argparse.ArgumentParser( | ||
formatter_class=argparse.ArgumentDefaultsHelpFormatter | ||
) | ||
parser.add_argument( | ||
"--model", | ||
required=True, | ||
help=( | ||
"Name of the pretrained model to download, " | ||
"or path to a directory containing the pretrained model." | ||
), | ||
) | ||
parser.add_argument("--output_dir", required=True, help="Output model directory.") | ||
parser.add_argument( | ||
"--inference_mode", | ||
required=True, | ||
choices=["causallm", "seq2seq"], | ||
help="Model inference mode. ", | ||
) | ||
parser.add_argument( | ||
"--prompt_template", default=None, help="prompt template for fim" | ||
) | ||
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return parser |
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from args import make_parser | ||
import json | ||
import os | ||
import shutil | ||
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from ctranslate2.converters.transformers import TransformersConverter | ||
from huggingface_hub import snapshot_download | ||
from transformers.convert_slow_tokenizers_checkpoints_to_fast import ( | ||
convert_slow_checkpoint_to_fast, | ||
) | ||
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class InvalidConvertionException(Exception): | ||
def __init__(self, *args: object) -> None: | ||
super().__init__(*args) | ||
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def convert_tokenizer(): | ||
if os.path.exists("./tokenizer.json"): | ||
print("found tokenizer.json, skipping tokenizer conversion") | ||
return | ||
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# Infer tokenizer name | ||
if not os.path.isfile("tokenizer_config.json"): | ||
raise InvalidConvertionException( | ||
"cannot find tokenizer_config.json, unable to infer tokenizer name" | ||
) | ||
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data = {} | ||
with open("tokenizer_config.json", "r", encoding="utf-8") as f: | ||
data = json.load(f) | ||
tokenizer_name = data["tokenizer_class"] | ||
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convert_tmp_dir = "./convert_tmp" | ||
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# Start to convert | ||
convert_slow_checkpoint_to_fast( | ||
tokenizer_name=tokenizer_name, | ||
checkpoint_name="./", | ||
dump_path=convert_tmp_dir, | ||
force_download=True, | ||
) | ||
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# After successful conversion, copy file from ./convert_tmp to ./ | ||
for root, dirs, files in os.walk(convert_tmp_dir): | ||
for f in files: | ||
fpath = os.path.join(root, f) | ||
shutil.copy2(fpath, "./") | ||
for d in dirs: | ||
dpath = os.path.join(root, d) | ||
shutil.copy2(dpath, "./") | ||
shutil.rmtree(convert_tmp_dir) | ||
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def generate_tabby_json(args): | ||
if os.path.exists("./tabby.json"): | ||
print("found tabby.json, skipping tabby.json generation") | ||
return | ||
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data = {} | ||
data["auto_model"] = ( | ||
"AutoModelForCausalLM" | ||
if args.inference_mode == "causallm" | ||
else "AutoModelForSeq2SeqLM" | ||
) | ||
if args.prompt_template: | ||
data["prompt_template"] = args.prompt_template | ||
with open("tabby.json", "w", encoding="utf-8") as f: | ||
json.dump(data, f, indent=4) | ||
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def main(): | ||
# Set up args | ||
parser = make_parser() | ||
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args = parser.parse_args() | ||
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# Check out model | ||
model_path = snapshot_download( | ||
repo_id=args.model, | ||
cache_dir=args.output_dir, | ||
force_download=False, | ||
) | ||
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os.chdir(model_path) | ||
convert_output_dir = os.path.join(model_path, "ctranslate2") | ||
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# Convert model into ctranslate | ||
converter = TransformersConverter( | ||
model_name_or_path=model_path, | ||
load_as_float16=True, | ||
trust_remote_code=True, | ||
) | ||
converter.convert( | ||
output_dir=convert_output_dir, vmap=None, quantization="float16", force=True | ||
) | ||
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# Convert model with fast tokenizer | ||
convert_tokenizer() | ||
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# Generate tabby.json | ||
generate_tabby_json(args) | ||
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if __name__ == "__main__": | ||
main() |
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ctranslate2 | ||
huggingface_hub | ||
transformers |