Datasets:
jblitzar
/
github-python

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parser.add_argument('--local_rank', type=int, default=-1)
parser.add_argument('--config', type=str)
parser = deepspeed.add_config_arguments(parser)
args = parser.parse_args()
device = torch.device('cuda', args.local_rank)
torch.cuda.set_device(args.local_rank)
ws = int(os.environ['WORLD_SIZE'])
rk = int(os.environ['RANK'])
deepspeed.init_distributed(dist_backend='nccl')
with open(args.config, 'r') as fr:
ds_cfg = yaml.load(fr, Loader=yaml.FullLoader)
model_name = ds_cfg['model_path']
if ds_cfg['from_scratch']:
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
else:
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
if ds_cfg['use_grad_ckpt']: model.gradient_checkpointing_enable()
model.train()
model.to(device)
training_data = TextDataSet(ds_cfg['data_path'],
ds_cfg['model_path'],
ds_cfg['max_seq_len'])
n_iters = ds_cfg['n_epoches'] * len(training_data) // ds_cfg['train_batch_size']
ds_cfg['scheduler']['params']['total_num_steps'] = n_iters
model_engine, optimizer, train_loader, lr_schdlr = deepspeed.initialize(
args=args, model=model,
# model_parameters=model.parameters(),
training_data=training_data,
config=ds_cfg,
)
print('num of samples: ', len(training_data))
print('num of iters: ', n_iters)
save_path = ds_cfg['save_path']
for e in range(ds_cfg['n_epoches']):
train_loader.data_sampler.set_epoch(e)
for i, batch in enumerate(train_loader):
batch = [el.cuda() for el in batch]
outputs = model_engine(input_ids=batch[0][..., 0],
attention_mask=batch[0][..., 1], labels=batch[1])
model_engine.backward(outputs.loss)
model_engine.step()
model_engine.save_checkpoint(save_path, client_state={'epoch': e})
# <FILESEP>
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import os
import shutil
import sys
import subprocess
import string
import random
import json
import re
import time
import argparse
import zipfile
from io import BytesIO
from concurrent.futures import ThreadPoolExecutor, as_completed
from utils.decorators import MessageDecorator
from utils.provider import APIProvider
try:
import requests
from colorama import Fore, Style
except ImportError:
print("\tSome dependencies could not be imported (possibly not installed)")
print(
"Type `pip3 install -r requirements.txt` to "
" install all required packages")
sys.exit(1)
def readisdc():
with open("isdcodes.json") as file:
isdcodes = json.load(file)
return isdcodes
def get_version():
try:
return open(".version", "r").read().strip()
except Exception:
return '1.0'