Support Bone

docs/source/_toctree.yml

@@ -118,6 +118,8 @@
       title: VB-LoRA
     - local: package_reference/hra
       title: HRA
+    - local: package_reference/bone
+      title: Bone
 
     title: Adapters
   - sections:

docs/source/conceptual_guides/adapter.md

@@ -117,4 +117,13 @@ To avoid adding noise to the tokens, the adapter uses zero-initialized attention
 
 HRA constructs a chain of `r` trainable Householder reflections (HRs). Because the Householder reflection matrix is an orthogonal matrix and the product of orthogonal matrices is also an orthogonal matrix, HRA satisfies the theoretical guarantee of Orthogonal Finetuning (OFT). Meanwhile, HRA can also be viewed as an low-rank fine-tuning adapter by rewriting formula. 
 
-The higher `r`, the more trainable parameters, resulting in a larger model capacity and better performance. Besides, due to the chain structure, the orthogonality of HR planes impacts the capacity and regularity of HRA. To achieve a trade-off between the model capacity and regularity, an orthogonality regularizer of the HR planes is added to the loss function. The weight \\(\lambda\\) can control the strength of the regularizer. 
+The higher `r`, the more trainable parameters, resulting in a larger model capacity and better performance. Besides, due to the chain structure, the orthogonality of HR planes impacts the capacity and regularity of HRA. To achieve a trade-off between the model capacity and regularity, an orthogonality regularizer of the HR planes is added to the loss function. The weight \\(\lambda\\) can control the strength of the regularizer. 
+
+## Bone
+[Bone](https://huggingface.co/papers/2409.15371) is a new PEFT technology different from the LoRA series, reducing training resources and requiring no complex initialization. Bone not only enhances the utilization of original weight information but also emphasizes the internal connections between weights, leading to faster convergence and better data fitting.
+
+<small><a href="https://huggingface.co/papers/2409.15371">Bone: Block Affine Transformation as Parameter Efficient Fine-tuning Methods for Large Language Models</a></small>
+
+Bone reduces the number of trainable parameters by using a block grouping method, and the block computation of weight W effectively promotes information exchange in the original weights, enhancing data fitting capability during fine-tuning. The experiment mentions controlling the size of trainable parameters through b (block size), similar to r (rank) in LoRA. For consistency within PEFT, we also name b as r. Note: Bone's r (b) is special and requires that weight W satisfies the conditions `in_features % r == 0` and `out_features % r == 0`. Additionally, when `in_features == out_features` and Bone-r equals LoRA-r, Bone's number of trainable parameters is only half that of LoRA.
+
+From the experiments in the paper, it is evident that Bone, with only half the parameters of LoRA, can outperform LoRA variants across various metrics. However, Bone currently has some issues: it is slower than LoRA and requires checkpointing to address excessive memory usage due to intermediate values, which further reduces training speed. We plan to address this in the future. Contributions are welcome.

docs/source/package_reference/bone.md

@@ -0,0 +1,31 @@
+<!--Copyright 2024 The HuggingFace 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Bone
+
+Block Affine ([Bone](https://huggingface.co/papers/2409.15371)) is a new PEFT technology different from the LoRA series, reducing training resources and requiring no complex initialization. Bone not only enhances the utilization of original weight information but also emphasizes the internal connections between weights, leading to faster convergence and better data fitting.
+
+The abstract from the paper is:
+
+Low-Rank Adaptation (LoRA) has achieved remarkable training results by freezing the original weights and training only low-rank matrices, establishing itself as the predominant fine-tuning method for LLMs. In pursuit of performance closer to full-parameter training, a series of LoRA variants have emerged, such as LoRA+, PISSA, Olora, and LoRA-GA. However, these improvements complicate the initial setup of model training and increase initialization time. More importantly, they overlook the internal interactions of the original weight information. To address these issues, we introduce a novel theory, ``Weight Guide'' aimed at continuously guiding trainable matrices through the original weights during training to enhance the utilization of weight information. Based on this theory, we designed a new PEFT technique called Bone (Block Affine), which not only enhances the utilization of original weight information but also emphasizes the internal connections between weights, leading to faster convergence and better data fitting. Experimental comparisons across two different LLM architectures (LLaMA2, RWKV6) and various parameter scales demonstrate that the Bone structure can achieve rapid convergence and superior data fitting without the need for complex initialization. For example, when fine-tuning LLaMA2-7B on the MetaMathQA dataset and validating on GSM8k and math benchmarks, Bone achieved fine-tuning scores of 49.36 and 8.8, respectively, outperforming PISSA by 5.84\% and 1.96\%.
+
+## BoneConfig
+
+[[autodoc]] tuners.bone.config.BoneConfig
+
+## BoneModel
+
+[[autodoc]] tuners.bone.model.BoneModel

examples/bone_finetuning/README.md

@@ -0,0 +1,87 @@
+# BONE: BLOCK AFFINE TRANSFORMATION AS PARAMETER EFFICIENT FINE-TUNING METHODS FOR LARGE LANGUAGE MODELS
+## Introduction ([Paper](https://arxiv.org/pdf/2409.15371), [code](https://github.com/JL-er/Bone))
+Low-Rank Adaptation (LoRA) has achieved remarkable training results by freezing the original weights and training only low-rank matrices, establishing itself as the predominant fine-tuning method for LLMs. In pursuit of performance closer to full-parameter training, a series of LoRA variants have emerged, such as LoRA+, PISSA, Olora, and LoRA-GA. However, these improvements complicate the initial setup of model training and increase initialization time. More importantly, they overlook the internal interactions of the original weight information. To address these issues, we introduce a novel theory, ``Weight Guide'' aimed at continuously guiding trainable matrices through the original weights during training to enhance the utilization of weight information. Based on this theory, we designed a new PEFT technique called Bone Block Affine, which not only enhances the utilization of original weight information but also emphasizes the internal connections between weights, leading to faster convergence and better data fitting. Experimental comparisons across two different LLM architectures (LLaMA2, RWKV6) and various parameter scales demonstrate that the Bone structure can achieve rapid convergence and superior data fitting without the need for complex initialization. For example, when fine-tuning LLaMA2-7B on the MetaMathQA dataset and validating on GSM8k and math benchmarks, Bone achieved fine-tuning scores of 49.36 and 8.8, respectively, outperforming PISSA by 5.84% and 1.96%.
+
+## Quick Start
+```python
+import torch
+from peft import LoraConfig, get_peft_model
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from trl import SFTConfig, SFTTrainer
+from datasets import load_dataset
+
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+tokenizer.pad_token_id = tokenizer.eos_token_id
+bone_config = BoneConfig(
+    r = 64
+)
+peft_model = get_peft_model(model, bone_config)
+
+peft_model.print_trainable_parameters()
+
+dataset = load_dataset("imdb", split="train[:1%]")
+
+training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+trainer = SFTTrainer(
+    model=peft_model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+trainer.train()
+peft_model.save_pretrained("bone-llama-2-7b")
+```
+
+
+To utilize the fine-tuned Bone modules, simply run the following command:
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b-hf", torch_dtype=torch.bfloat16, device_map="auto"
+)
+peft_model = PeftModel.from_pretrained(model, "bone-llama-2-7b")
+```
+
+## Advanced Usage
+
+### Fine-tune 
+```shell
+python bone_finetuning.py \
+    --base_model_name_or_path meta-llama/Llama-2-7b-hf \
+    --output_dir output/bone-llama-2-7b-metamath-10k \
+    --bits bf16 \
+    --data_path meta-math/MetaMathQA \
+    --dataset_split train[:100000] \
+    --dataset_field query response \
+    --bf16 True \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 8 \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 1 \
+    --logging_steps 1 \
+    --learning_rate 2e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --tf32 True \
+    --report_to none
+```
+
+
+
+# Citation
+```bib
+@misc{kang2024boneblockaffinetransformation,
+      title={Bone: Block Affine Transformation as Parameter Efficient Fine-tuning Methods for Large Language Models}, 
+      author={Jiale Kang},
+      year={2024},
+      eprint={2409.15371},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.15371}, 
+}

examples/bone_finetuning/bone_finetuning.py

@@ -0,0 +1,99 @@
+# Copyright 2023-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.
+
+import os
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
+from trl import SFTConfig, SFTTrainer
+
+from peft import BoneConfig, get_peft_model
+
+
+@dataclass
+class ScriptArguments(SFTConfig):
+    # model configs
+    base_model_name_or_path: Optional[str] = field(
+        default=None, metadata={"help": "The name or path of the fp32/16 base model."}
+    )
+    bits: str = field(default="bf16", metadata={"help": "(`['bf16', 'fp16', fp32]`)"})
+    init_bone_weights: str = field(default="False")
+    bone_r: int = field(default=16)
+    merge_and_save: bool = field(default=False)
+    # dataset configs
+    data_path: str = field(default="imdb", metadata={"help": "Path to the training data."})
+    dataset_split: str = field(default="train[:1%]", metadata={"help": "(`['train', 'test', 'eval']`):"})
+    dataset_field: list[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
+
+
+parser = HfArgumentParser(ScriptArguments)
+script_args = parser.parse_args_into_dataclasses()[0]
+print(script_args)
+
+print(f"Load pre-processed residual model in {script_args.bits} bits.")
+if script_args.bits in ["nf4", "fp4", "int8"]:
+    print("Bone currently does not support quantization.")
+
+elif script_args.base_model_name_or_path is not None:
+    print(f"No available pre-processed model, manually initialize a Bone using {script_args.base_model_name_or_path}.")
+    model = AutoModelForCausalLM.from_pretrained(
+        script_args.base_model_name_or_path,
+        torch_dtype=(
+            torch.float16
+            if script_args.bits == "fp16"
+            else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
+        ),
+        device_map="auto",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+    lora_config = BoneConfig(
+        r=script_args.bone_r,
+        target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+    peft_model = get_peft_model(model, lora_config)
+
+print(peft_model)
+peft_model.print_trainable_parameters()
+
+print(f"Training Bone with trl on the {script_args.data_path}[{script_args.dataset_split}] dataset.")
+dataset = load_dataset(script_args.data_path, split=script_args.dataset_split)
+dataset = dataset.map(
+    lambda example: {
+        "text": f"### USER: {example[script_args.dataset_field[0]]}\n### ASSISTANT: {example[script_args.dataset_field[1]]}"
+    }
+)
+
+trainer = SFTTrainer(
+    model=peft_model,
+    args=script_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+trainer.train()
+trainer.save_state()
+
+peft_model.save_pretrained(
+    os.path.join(script_args.output_dir, "bone_ft"),
+)
+
+if script_args.merge_and_save:
+    model = peft_model.merge_and_unload()
+    model.save_pretrained(os.path.join(script_args.output_dir, "bone_merged"))
+    tokenizer.save_pretrained(os.path.join(script_args.output_dir, "bone_merged"))

src/peft/__init__.py

@@ -91,6 +91,8 @@
     HRAConfig,
     HRAModel,
     VBLoRAConfig,
+    BoneConfig,
+    BoneModel,
 )
 from .utils import (
     TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,

src/peft/mapping.py

@@ -38,6 +38,8 @@
     AdaptionPromptConfig,
     BOFTConfig,
     BOFTModel,
+    BoneConfig,
+    BoneModel,
     FourierFTConfig,
     FourierFTModel,
     HRAConfig,
@@ -104,6 +106,7 @@
     "XLORA": XLoraConfig,
     "HRA": HRAConfig,
     "VBLORA": VBLoRAConfig,
+    "BONE": BoneConfig,
 }
 
 PEFT_TYPE_TO_TUNER_MAPPING: dict[str, type[BaseTuner]] = {
@@ -121,6 +124,7 @@
     "XLORA": XLoraModel,
     "HRA": HRAModel,
     "VBLORA": VBLoRAModel,
+    "BONE": BoneModel,
 }
 
 

src/peft/peft_model.py

@@ -46,6 +46,7 @@
     AdaLoraModel,
     AdaptionPromptModel,
     BOFTModel,
+    BoneModel,
     FourierFTModel,
     HRAModel,
     IA3Model,
@@ -104,6 +105,7 @@
     PeftType.XLORA: XLoraModel,
     PeftType.HRA: HRAModel,
     PeftType.VBLORA: VBLoRAModel,
+    PeftType.BONE: BoneModel,
 }
 
 

src/peft/tuners/__init__.py

@@ -37,3 +37,4 @@
 from .xlora import XLoraConfig, XLoraModel
 from .hra import HRAConfig, HRAModel
 from .vblora import VBLoRAConfig, VBLoRAModel
+from .bone import BoneConfig, BoneModel

src/peft/tuners/bone/__init__.py

@@ -0,0 +1,20 @@
+# 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.
+
+from .config import BoneConfig
+from .layer import BoneLayer, BoneLinear
+from .model import BoneModel
+
+
+__all__ = ["BoneConfig", "BoneModel", "BoneLinear", "BoneLayer"]