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Make paths cross-platform (should work on Windows now)

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oobabooga 2023-01-07 16:33:43 -03:00
parent 89fb0a13c5
commit 5345685ead
3 changed files with 28 additions and 31 deletions
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9
convert-to-torch.py
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@ -10,11 +10,10 @@ Output will be written to torch-dumps/name-of-the-model.pt
from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
import torch
from sys import argv
from pathlib import Path
path = argv[1]
if path[-1] != '/':
path = path+'/'
model_name = path.split('/')[-2]
path = Path(argv[1])
model_name = path.name
print(f"Loading {model_name}...")
if model_name in ['flan-t5', 't5-large']:
@ -24,4 +23,4 @@ else:
print("Model loaded.")
print(f"Saving to torch-dumps/{model_name}.pt")
torch.save(model, f"torch-dumps/{model_name}.pt")
torch.save(model, Path(f"torch-dumps/{model_name}.pt"))

14
download-model.py
View file

@ -9,16 +9,16 @@ python download-model.py facebook/opt-1.3b
import requests
from bs4 import BeautifulSoup
import multiprocessing
import os
import tqdm
from sys import argv
from pathlib import Path
def get_file(args):
url = args[0]
output_folder = args[1]
r = requests.get(url, stream=True)
with open(f"{output_folder}/{url.split('/')[-1]}", 'wb') as f:
with open(output_folder / Path(url.split('/')[-1]), 'wb') as f:
total_size = int(r.headers.get('content-length', 0))
block_size = 1024
t = tqdm.tqdm(total=total_size, unit='iB', unit_scale=True)
@ -27,13 +27,11 @@ def get_file(args):
f.write(data)
t.close()
model = argv[1]
if model.endswith('/'):
model = model[:-1]
model = Path(argv[1])
url = f'https://huggingface.co/{model}/tree/main'
output_folder = f"models/{model.split('/')[-1]}"
if not os.path.exists(output_folder):
os.mkdir(output_folder)
output_folder = Path("models") / model.name
if not output_folder.exists():
output_folder.mkdir()
# Finding the relevant files to download
page = requests.get(url)

36
server.py
View file

@ -1,15 +1,15 @@
import os
import re
import time
import glob
from sys import exit
import torch
import argparse
from pathlib import Path
import gradio as gr
import transformers
from html_generator import *
from transformers import AutoTokenizer
from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
from transformers import AutoTokenizer, T5Tokenizer
from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
parser = argparse.ArgumentParser()
@ -17,37 +17,37 @@ parser.add_argument('--model', type=str, help='Name of the model to load by defa
parser.add_argument('--notebook', action='store_true', help='Launch the webui in notebook mode, where the output is written to the same text box as the input.')
args = parser.parse_args()
loaded_preset = None
available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*")+ glob.glob("torch-dumps/*"))))
available_models = sorted(set(map(lambda x : str(x.name).replace('.pt', ''), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*')))))
available_models = [item for item in available_models if not item.endswith('.txt')]
#available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*[!\.][!t][!x][!t]")+ glob.glob("torch-dumps/*[!\.][!t][!x][!t]"))))
available_presets = sorted(set(map(lambda x : str(x.name).split('.')[0], list(Path('presets').glob('*.txt')))))
def load_model(model_name):
print(f"Loading {model_name}...")
t0 = time.time()
# Loading the model
if os.path.exists(f"torch-dumps/{model_name}.pt"):
if Path(f"torch-dumps/{model_name}.pt").exists():
print("Loading in .pt format...")
model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
model = torch.load(Path(f"torch-dumps/{model_name}.pt")).cuda()
elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')):
if any(size in model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
else:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
elif model_name in ['gpt-j-6B']:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
elif model_name in ['flan-t5', 't5-large']:
model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
else:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
# Loading the tokenizer
if model_name.startswith('gpt4chan'):
tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
elif model_name in ['flan-t5']:
tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")
tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/"))
else:
tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/")
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
return model, tokenizer
@ -78,7 +78,7 @@ def generate_reply(question, temperature, max_length, inference_settings, select
torch.cuda.empty_cache()
model, tokenizer = load_model(model_name)
if inference_settings != loaded_preset:
with open(f'presets/{inference_settings}.txt', 'r') as infile:
with open(Path(f'presets/{inference_settings}.txt'), 'r') as infile:
preset = infile.read()
loaded_preset = inference_settings
@ -143,7 +143,7 @@ if args.notebook:
temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
with gr.Column():
preset_menu = gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="NovelAI-Sphinx Moth", label='Preset')
preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=False)
@ -161,7 +161,7 @@ else:
textbox = gr.Textbox(value=default_text, lines=15, label='Input')
temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
preset_menu = gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="NovelAI-Sphinx Moth", label='Preset')
preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
btn = gr.Button("Generate")
with gr.Column():