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FEAT Add OLoRA initialization strategy to LoRA (#1828)
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# OLoRA: Orthonormal Low Rank Adaptation of Large Language Models | ||
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## Introduction | ||
[OLoRA](https://arxiv.org/abs/2406.01775) is a novel approach that leverages orthonormal low rank adaptation through QR decomposition. Unlike the default LoRA implementation, OLoRA decomposes original weights into their $\mathbf{Q}$ and $\mathbf{R}$ parts, and then uses the first `rank` rows of $\mathbf{R}$ and the first `rank` columns of $\mathbf{Q}$ to initialize $\mathbf{A}$ and $\mathbf{B}$, respectively. This results in significantly faster convergence, more stable training, and superior performance. | ||
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## Quick start | ||
```python | ||
import torch | ||
from peft import LoraConfig, get_peft_model | ||
from transformers import AutoTokenizer, AutoModelForCausalLM | ||
from trl import SFTTrainer | ||
from datasets import load_dataset | ||
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.bfloat16, device_map="auto") | ||
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") | ||
dataset = load_dataset("imdb", split="train[:1%]") | ||
lora_config = LoraConfig( | ||
init_lora_weights="olora" | ||
) | ||
peft_model = get_peft_model(model, lora_config) | ||
trainer = SFTTrainer( | ||
model=peft_model, | ||
train_dataset=dataset, | ||
dataset_text_field="text", | ||
max_seq_length=512, | ||
tokenizer=tokenizer, | ||
) | ||
trainer.train() | ||
peft_model.save_pretrained("olora-opt-350m") | ||
``` | ||
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There is no additional change needed to your standard LoRA procedure, except for specifying `init_lora_weights = "olora"` option in your lora configuration. | ||
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Additionally you can refer to olora finetuning script. | ||
Run the script simply by running: | ||
```bash | ||
python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m | ||
``` | ||
OLoRA also supports quantization. To use 4-bit quantization try: | ||
```bash | ||
python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m --quantize | ||
``` | ||
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## Use the model | ||
You can load and use the model as any other 🤗 PEFT model | ||
```python | ||
from peft import PeftModel | ||
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") | ||
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") | ||
olora_model = PeftModel.from_pretrained(model, "olora-opt-350m") | ||
``` | ||
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## OLoRA and LoRA | ||
OLoRA differs from LoRA in that it mutates the original weights. To utilize multiple adapters simultaneously, you can leverage the `path_initial_model_for_weight_conversion` option. Below is a simple template illustrating how to convert OLoRA to conventional LoRA: | ||
```python | ||
base_model = AutoModel.from_pretrained("facebook/opt-350m") | ||
olora_config = LoraConfig( | ||
... | ||
init_lora_weights = "olora" # Initialize the model with OLoRA | ||
) | ||
olora_model = get_peft_model(base_model, olora_config) | ||
init_path = <path-to-untrained-olora-model> | ||
olora_model.save_pretrained(init_path) # Save the model *before* performing any training | ||
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# Train the model | ||
train(olora_model) # Your training loop | ||
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#Save the model after training | ||
olora_model.save_pretrained(output_dir, path_initial_model_for_weight_conversion=init_path) | ||
``` | ||
After completing training, you can save and convert your OLoRA model to a conventional LoRA model by setting `path_initial_model_for_weight_conversion` to `init_path`, that is the path of your untrained OLoRA model. This conversion enables you to use multiple adapters with your LoRA model. | ||
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## Citation | ||
``` | ||
@misc{büyükakyüz2024olora, | ||
title={OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models}, | ||
author={Kerim Büyükakyüz}, | ||
year={2024}, | ||
eprint={2406.01775}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.CL} | ||
} | ||
``` |
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# Copyright 2024-present the HuggingFace Inc. team. | ||
# | ||
# 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. | ||
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from typing import List | ||
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import torch | ||
import transformers | ||
from datasets import load_dataset | ||
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | ||
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from peft import ( | ||
LoraConfig, | ||
get_peft_model, | ||
) | ||
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def train( | ||
base_model: str = "path/to/model", | ||
data_path: str = "yahma/alpaca-cleaned", | ||
output_dir: str = "olora", | ||
batch_size: int = 16, | ||
num_epochs: int = 1, | ||
learning_rate: float = 3e-4, | ||
cutoff_len: int = 256, | ||
val_set_size: int = 16, | ||
quantize: bool = False, | ||
eval_step: int = 100, | ||
save_step: int = 100, | ||
device_map: str = "auto", | ||
lora_r: int = 32, | ||
lora_alpha: int = 16, | ||
lora_dropout: float = 0.05, | ||
lora_target_modules: List[str] = None, | ||
init_lora_weights="olora", | ||
): | ||
model = AutoModelForCausalLM.from_pretrained( | ||
base_model, | ||
device_map=device_map, | ||
quantization_config=BitsAndBytesConfig( | ||
load_in_4bit=True, | ||
bnb_4bit_compute_dtype=torch.bfloat16, | ||
bnb_4bit_use_double_quant=True, | ||
bnb_4bit_quant_type="nf4", | ||
) | ||
if quantize | ||
else None, | ||
torch_dtype=torch.float16, | ||
) | ||
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tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True) | ||
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def tokenize(prompt, add_eos_token=True): | ||
result = tokenizer( | ||
prompt, | ||
truncation=True, | ||
max_length=cutoff_len, | ||
padding=False, | ||
return_tensors=None, | ||
) | ||
if ( | ||
result["input_ids"][-1] != tokenizer.eos_token_id | ||
and len(result["input_ids"]) < cutoff_len | ||
and add_eos_token | ||
): | ||
result["input_ids"].append(tokenizer.eos_token_id) | ||
result["attention_mask"].append(1) | ||
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result["labels"] = result["input_ids"].copy() | ||
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return result | ||
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def generate_and_tokenize_prompt(example): | ||
full_prompt = generate_prompt(example) | ||
tokenized_full_prompt = tokenize(full_prompt) | ||
return tokenized_full_prompt | ||
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config = LoraConfig( | ||
r=lora_r, | ||
lora_alpha=lora_alpha, | ||
target_modules=lora_target_modules, | ||
lora_dropout=lora_dropout, | ||
bias="none", | ||
task_type="CAUSAL_LM", | ||
init_lora_weights=init_lora_weights, | ||
) | ||
model = get_peft_model(model, config) | ||
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data = load_dataset(data_path) | ||
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train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42) | ||
train_data = train_val["train"].shuffle().map(generate_and_tokenize_prompt) | ||
val_data = train_val["test"].shuffle().map(generate_and_tokenize_prompt) | ||
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trainer = transformers.Trainer( | ||
model=model, | ||
train_dataset=train_data, | ||
eval_dataset=val_data, | ||
args=transformers.TrainingArguments( | ||
per_device_train_batch_size=batch_size, | ||
warmup_steps=100, | ||
num_train_epochs=num_epochs, | ||
learning_rate=learning_rate, | ||
fp16=True, | ||
logging_steps=100, | ||
optim="adamw_torch", | ||
evaluation_strategy="steps", | ||
save_strategy="steps", | ||
eval_steps=eval_step, | ||
save_steps=save_step, | ||
output_dir=output_dir, | ||
save_total_limit=3, | ||
load_best_model_at_end=True, | ||
), | ||
data_collator=transformers.DataCollatorForSeq2Seq( | ||
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True | ||
), | ||
) | ||
trainer.train() | ||
model.save_pretrained(output_dir) | ||
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def generate_prompt(example): | ||
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | ||
### Instruction: | ||
{example["instruction"]} | ||
### Response: | ||
{example["output"]}""" | ||
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if __name__ == "__main__": | ||
import argparse | ||
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parser = argparse.ArgumentParser() | ||
parser.add_argument("--base_model", type=str, default="path/to/model") | ||
parser.add_argument("--data_path", type=str, default="yahma/alpaca-cleaned") | ||
parser.add_argument("--output_dir", type=str, default="olora") | ||
parser.add_argument("--batch_size", type=int, default=16) | ||
parser.add_argument("--num_epochs", type=int, default=1) | ||
parser.add_argument("--learning_rate", type=float, default=3e-4) | ||
parser.add_argument("--cutoff_len", type=int, default=256) | ||
parser.add_argument("--val_set_size", type=int, default=16) | ||
parser.add_argument("--quantize", action="store_true") | ||
parser.add_argument("--eval_step", type=int, default=100) | ||
parser.add_argument("--save_step", type=int, default=100) | ||
parser.add_argument("--device_map", type=str, default="auto") | ||
parser.add_argument("--lora_r", type=int, default=32) | ||
parser.add_argument("--lora_alpha", type=int, default=16) | ||
parser.add_argument("--lora_dropout", type=float, default=0.05) | ||
parser.add_argument("--lora_target_modules", type=str, default=None) | ||
parser.add_argument("--init_lora_weights", type=str, default="olora") | ||
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args = parser.parse_args() | ||
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train( | ||
base_model=args.base_model, | ||
data_path=args.data_path, | ||
output_dir=args.output_dir, | ||
batch_size=args.batch_size, | ||
num_epochs=args.num_epochs, | ||
learning_rate=args.learning_rate, | ||
cutoff_len=args.cutoff_len, | ||
val_set_size=args.val_set_size, | ||
quantize=args.quantize, | ||
eval_step=args.eval_step, | ||
save_step=args.save_step, | ||
device_map=args.device_map, | ||
lora_r=args.lora_r, | ||
lora_alpha=args.lora_alpha, | ||
lora_dropout=args.lora_dropout, | ||
lora_target_modules=args.lora_target_modules, | ||
init_lora_weights=args.init_lora_weights, | ||
) |
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