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train.py
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# Copyright 2023 Baichuan Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import argparse
import deepspeed
import deepspeed.comm as dist
import numpy as np
import sentencepiece as spm
import torch
from models.configuration_baichuan import BaiChuanConfig
from models.modeling_baichuan import BaiChuanForCausalLM
def get_argument_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default="data_dir",
help="Text files to do pre-train on")
parser.add_argument("--tokenizer_path", type=str,
default="tokenizer.model",
help="Tokenizer model file path")
parser.add_argument("--max_length", type=int, default=4096,
help="Max tokens per sentence in corpus")
parser.add_argument("--steps_per_epoch", type=int, default=4096,
help="Step intervals to save checkpoint")
parser.add_argument("--checkpoint_saving_path", type=str,
default=&quo
8000
t;checkpoints",
help="Path to store checkpoint files")
parser.add_argument("--local_rank", type=int, default=-1,
help="Reserved for deepspeed framework")
return parser
arg_parser = get_argument_parser()
arg_parser = deepspeed.add_config_arguments(arg_parser)
args = arg_parser.parse_args()
deepspeed.init_distributed()
class DataEngine():
def __init__(self, data_dir, tokenizer_path, micro_batch_size, max_length):
self.MIN_TEXT_LEN = 20
self.EOS_TOKEN_ID = 2
self.data_dir = data_dir
self.sp = spm.SentencePieceProcessor()
self.sp.Load(tokenizer_path)
self.micro_batch_size = micro_batch_size
self.max_length = max_length
self.data = []
self.global_input_paths = [self.data_dir + "/" + x
for x in os.listdir(self.data_dir)]
self.local_input_paths = [x for i, x in
enumerate(self.global_input_paths)
if i % dist.get_world_size() == dist.get_rank()]
def load_data(self):
for file_path in self.local_input_paths:
data = []
with open(file_path, encoding="utf-8", errors="ignore") as f:
for line_id, line in enumerate(f):
cc = self.sp.EncodeAsIds(line.strip()) + [self.EOS_TOKEN_ID]
if len(cc) < self.MIN_TEXT_LEN:
cc = []
data.extend(cc)
if len(data) >= self.micro_batch_size * (self.max_length + 1):
index = self.micro_batch_size * (self.max_length + 1)
self.data.append(data[:index])
data = []
return
def get_data(self):
data = self.data.pop(0)
seq = np.asarray(data).reshape(self.micro_batch_size, self.max_length + 1)
data = torch.LongTensor(seq)
data = data.cuda(non_blocking=True)
return data
def prepare_data():
data_dir = args.data_dir
tokenizer_path = args.tokenizer_path
ds_config = json.load(open(args.deepspeed_config))
micro_batch_size = ds_config["train_micro_batch_size_per_gpu"]
max_length = args.max_length
data_engine = DataEngine(data_dir, tokenizer_path, micro_batch_size, max_length)
data_engine.load_data()
return data_engine
def prepare_model():
with deepspeed.zero.Init(config_dict_or_path=args.deepspeed_config,
enabled=True,
mem_efficient_linear=False,
mpu=None):
model = BaiChuanForCausalLM(BaiChuanConfig())
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
model_engine, _, _, _ = deepspeed.initialize(args=args,
model=model,
optimizer=None,
model_parameters=model_parameters)
return model_engine
def train(data_engine, model_engine):
model_engine.train()
step = 0
while step < args.steps_per_epoch:
data = data_engine.get_data()
loss = model_engine(data, labels=data).loss
model_engine.backward(loss)
model_engine.step()
step += 1
return
if __name__ == "__main__":
data_engine = prepare_data()
model_engine = prepare_model()
epoch = 0
while True:
train(data_engine, model_engine)
epoch += 1
model_engine.save_checkpoint(f"{args.checkpoint_saving_path}",
tag=f"Epoch-{epoch}")