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# coding=utf-8
# 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.
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
from trl import SFTTrainer
tqdm.pandas()
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with SFTTrainer
"""
model_name: Optional[str] = field(default="facebook/opt-350m", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(
default="timdettmers/openassistant-guanaco", metadata={"help": "the dataset name"}
)
dataset_text_field: Optional[str] = field(default="text", metadata={"help": "the text field of the dataset"})
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=1.41e-5, metadata={"help": "the learning rate"})
batch_size: Optional[int] = field(default=8, metadata={"help": "the batch size"}) # 64 original
seq_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
gradient_accumulation_steps: Optional[int] = field(
default=2, metadata={"help": "the number of gradient accumulation steps"}
)
load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
use_peft: Optional[bool] = field(default=False, metadata={"help": "Wether to use PEFT or not to train adapters"})
trust_remote_code: Optional[bool] = field(default=True, metadata={"help": "Enable `trust_remote_code`"})
output_dir: Optional[str] = field(default="./", metadata={"help": "the output directory"})
peft_lora_r: Optional[int] = field(default=8, metadata={"help": "the r parameter of the LoRA adapters"})
peft_lora_alpha: Optional[int] = field(default=2, metadata={"help": "the alpha parameter of the LoRA adapters"})
logging_steps: Optional[int] = field(default=1, metadata={"help": "the number of logging steps"})
use_auth_token: Optional[bool] = field(default=True, metadata={"help": "Use HF auth token to access the model"})
num_train_epochs: Optional[int] = field(default=2, metadata={"help": "the number of training epochs"})
max_steps: Optional[int] = field(default=-1, metadata={"help": "the number of training steps"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# Step 1: Load the model
if script_args.load_in_8bit and script_args.load_in_4bit:
raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif script_args.load_in_8bit or script_args.load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_8bit=script_args.load_in_8bit, load_in_4bit=script_args.load_in_4bit
)
# This means: fit the entire model on the GPU:0
device_map = {"": 0}
torch_dtype = torch.bfloat16
else:
device_map = None
quantization_config = None
torch_dtype = None
model = AutoModelForCausalLM.from_pretrained(
script_args.model_name,
quantization_config=quantization_config,
device_map=device_map,
trust_remote_code=script_args.trust_remote_code,
torch_dtype=torch_dtype,
use_auth_token=script_args.use_auth_token,
)
# Step 2: Load the dataset
dataset = load_dataset(script_args.dataset_name, split="train")
# Step 3: Define the training arguments
training_args = TrainingArguments(
output_dir=script_args.output_dir,
per_device_train_batch_size=script_args.batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
learning_rate=script_args.learning_rate,
logging_steps=script_args.logging_steps,
num_train_epochs=script_args.num_train_epochs,
max_steps=script_args.max_steps,
report_to=script_args.log_with,
)
# Step 4: Define the LoraConfig
if script_args.use_peft:
peft_config = LoraConfig(
r=script_args.peft_lora_r,
lora_alpha=script_args.peft_lora_alpha,
bias="none",
task_type="CAUSAL_LM",
)
else:
peft_config = None
# Step 5: Define the Trainer
trainer = SFTTrainer(
model=model,
args=training_args,
max_seq_length=script_args.seq_length,
train_dataset=dataset,
dataset_text_field=script_args.dataset_text_field,
peft_config=peft_config,
)
trainer.train()
# Step 6: Save the model
trainer.save_model(script_args.output_dir) |