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# coding=utf-8
# Original Scripts are
# https://gist.github.com/SunMarc/dcdb499ac16d355a8f265aa497645996
# and
# https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da
# changed by webbigdata for use_safetensors.
# Copyright 2023 The HuggingFace Inc. team. 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 os
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from peft import LoraConfig
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
AutoTokenizer,
TrainingArguments,
)
from peft import prepare_model_for_kbit_training, get_peft_model
from transformers import GPTQConfig
from trl import SFTTrainer
# This example fine-tunes Llama 2 model on Guanaco dataset
# using GPTQ and peft.
# Use it by correctly passing --model_name argument when running the
# script. The default model is ybelkada/llama-7b-GPTQ-test
# Versions used:
# accelerate == 0.21.0
# auto-gptq == 0.4.2
# trl == 0.4.7
# peft from source
# transformers from source
# optimum from source
# For models that have `config.pretraining_tp > 1` install:
# pip install git+https://github.com/huggingface/transformers.git
@dataclass
class ScriptArguments:
"""
These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train.
"""
local_rank: Optional[int] = field(default=-1, metadata={"help": "Used for multi-gpu"})
per_device_train_batch_size: Optional[int] = field(default=4)
per_device_eval_batch_size: Optional[int] = field(default=1)
gradient_accumulation_steps: Optional[int] = field(default=4)
learning_rate: Optional[float] = field(default=2e-4)
max_grad_norm: Optional[float] = field(default=0.3)
weight_decay: Optional[int] = field(default=0.001)
lora_alpha: Optional[int] = field(default=16)
lora_dropout: Optional[float] = field(default=0.1)
lora_r: Optional[int] = field(default=64)
max_seq_length: Optional[int] = field(default=512)
model_name: Optional[str] = field(
default="dahara1/weblab-10b-instruction-sft-GPTQ/finetune_sample",
metadata={
"help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc."
}
)
dataset_name: Optional[str] = field(
default="timdettmers/openassistant-guanaco",
metadata={"help": "The preference dataset to use."},
)
num_train_epochs: Optional[int] = field(
default=1,
metadata={"help": "The number of training epochs for the reward model."},
)
fp16: Optional[bool] = field(
default=False,
metadata={"help": "Enables fp16 training."},
)
bf16: Optional[bool] = field(
default=False,
metadata={"help": "Enables bf16 training."},
)
packing: Optional[bool] = field(
default=False,
metadata={"help": "Use packing dataset creating."},
)
gradient_checkpointing: Optional[bool] = field(
default=True,
metadata={"help": "Enables gradient checkpointing."},
)
optim: Optional[str] = field(
default="adamw_hf",
metadata={"help": "The optimizer to use."},
)
lr_scheduler_type: str = field(
default="constant",
metadata={"help": "Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis"},
)
max_steps: int = field(default=10000, metadata={"help": "How many optimizer update steps to take"})
warmup_ratio: float = field(default=0.03, metadata={"help": "Fraction of steps to do a warmup for"})
group_by_length: bool = field(
default=True,
metadata={
"help": "Group sequences into batches with same length. Saves memory and speeds up training considerably."
},
)
save_steps: int = field(default=10, metadata={"help": "Save checkpoint every X updates steps."})
logging_steps: int = field(default=10, metadata={"help": "Log every X updates steps."})
merge_and_push: Optional[bool] = field(
default=False,
metadata={"help": "Merge and push weights after training"},
)
output_dir: str = field(
default="./results",
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
def create_and_prepare_model(args):
major, _ = torch.cuda.get_device_capability()
if major >= 8:
print("=" * 80)
print("Your GPU supports bfloat16, you can accelerate training with the argument --bf16")
print("=" * 80)
# Load the entire model on the GPU 0
#device_map = {"":0}
# switch to `device_map = "auto"` for multi-GPU
device_map = "auto"
# need to disable exllama kernel
# exllama kernel are not very stable for training
model = AutoModelForCausalLM.from_pretrained(
args.model_name,
device_map=device_map,
use_safetensors=True,
quantization_config= GPTQConfig(bits=4, disable_exllama=True)
)
# check: https://github.com/huggingface/transformers/pull/24906
model.config.pretraining_tp = 1
peft_config = LoraConfig(
lora_alpha=script_args.lora_alpha,
lora_dropout=script_args.lora_dropout,
r=script_args.lora_r,
bias="none",
task_type="CAUSAL_LM",
)
tokenizer = AutoTokenizer.from_pretrained(script_args.model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
return model, peft_config, tokenizer
training_arguments = TrainingArguments(
output_dir=script_args.output_dir,
per_device_train_batch_size=script_args.per_device_train_batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
optim=script_args.optim,
save_steps=script_args.save_steps,
logging_steps=script_args.logging_steps,
learning_rate=script_args.learning_rate,
fp16=script_args.fp16,
bf16=script_args.bf16,
max_grad_norm=script_args.max_grad_norm,
max_steps=script_args.max_steps,
warmup_ratio=script_args.warmup_ratio,
group_by_length=script_args.group_by_length,
lr_scheduler_type=script_args.lr_scheduler_type,
)
model, peft_config, tokenizer = create_and_prepare_model(script_args)
model = prepare_model_for_kbit_training(model)
model = get_peft_model(model, peft_config)
model.config.use_cache = False
dataset = load_dataset("csv", data_files="jawiki3.csv", split='train')
# Fix weird overflow issue with fp16 training
tokenizer.padding_side = "right"
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
dataset_text_field="QuestionAnswer",
max_seq_length=script_args.max_seq_length,
tokenizer=tokenizer,
args=training_arguments,
packing=script_args.packing,
)
trainer.train()
if script_args.merge_and_push:
output_dir = os.path.join(script_args.output_dir, "final_checkpoints")
trainer.model.save_pretrained(output_dir)
# Free memory for merging weights
del model
torch.cuda.empty_cache()