mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-30 14:10:14 +01:00
39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
'''
|
|
Converts a transformers model to .pt, which is faster to load.
|
|
|
|
Run with python convert.py /path/to/model/
|
|
Make sure to write /path/to/model/ with a trailing / and not
|
|
/path/to/model
|
|
|
|
Output will be written to torch-dumps/name-of-the-model.pt
|
|
'''
|
|
|
|
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, AutoTokenizer, set_seed
|
|
from transformers import GPT2Tokenizer, GPT2Model, T5Tokenizer, T5ForConditionalGeneration
|
|
import torch
|
|
import sys
|
|
from sys import argv
|
|
import time
|
|
import glob
|
|
import psutil
|
|
|
|
print(f"torch-dumps/{argv[1].split('/')[-2]}.pt")
|
|
|
|
if argv[1].endswith('pt'):
|
|
model = OPTForCausalLM.from_pretrained(argv[1], device_map="auto")
|
|
torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
|
|
elif 'galactica' in argv[1].lower():
|
|
model = OPTForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
|
|
#model = OPTForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, load_in_8bit=True)
|
|
torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
|
|
elif 'flan-t5' in argv[1].lower():
|
|
model = T5ForConditionalGeneration.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
|
|
torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
|
|
else:
|
|
print("Loading the model")
|
|
model = AutoModelForCausalLM.from_pretrained(argv[1], low_cpu_mem_usage=True, torch_dtype=torch.float16)
|
|
print("Model loaded")
|
|
#model = AutoModelForCausalLM.from_pretrained(argv[1], device_map='auto', load_in_8bit=True)
|
|
torch.save(model, f"torch-dumps/{argv[1].split('/')[-2]}.pt")
|
|
|