Add new feature of SafeLoRA

examples/safelora/README.md

@@ -0,0 +1,46 @@
+# Safe LoRA 
+
+The official code of Safe LoRA: The Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
+
+## Quick Start
+
+### Get Weights with SafeLoRA
+Please import the `SafeLoraConfig` and `apply_safelora` first.
+Then, fill in the paths for the base, aligned, and PEFT models according to your needs. There are two types of `select_layers_type`: `threshold` and `number`. The `threshold` type will determine how many layers will be projected based on the value you set. The `number` type directly specifies the number of projected layers. `save_weights=True` will save and replace your original peft model weights.
+
+```python
+
+from peft.utils.safelora import SafeLoraConfig, apply_safelora
+
+peft_path = "../finetuneLLM/finetuned_models/samsumBad-7b-fp16-peft-seed-42"
+config = SafeLoraConfig(
+    base_model_path="meta-llama/Llama-2-7b-hf",
+    aligned_model_path="TheBloke/Llama-2-7B-Chat-fp16",
+    peft_model_path=peft_path,
+    device="cuda",
+    select_layers_type="threshold",
+    save_weights=True,
+)
+final_lora_weight = apply_safelora(config)
+
+```
+### Save SafeLoRA's Weights
+If you set `save_weights=False`, but still want to save the weights, you can use the following code.
+
+```python
+from safetensors.torch import save_file
+
+path = ...  # your PEFT model path
+save_file(final_lora_weight, os.path.join(path, "adapter_model.safetensors"))
+```
+
+### Use SafeLoRA Model
+Next, you can load the base model of the Peft Model along with the Peft model itself to use a model that has both downstream task utility and alignment.
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftConfig, PeftModel
+
+model = AutoModelForCausalLM.from_pretrained(<base-model-id>)
+model = PeftModel.from_pretrained(model, <SafeLoRA-path>)
+```

examples/safelora/safelora_inference.py

@@ -0,0 +1,28 @@
+import os
+
+from safetensors.torch import save_file
+from transformers import AutoModelForCausalLM
+
+from peft import PeftModel
+from peft.utils.safelora import SafeLoraConfig, apply_safelora
+
+
+peft_path = "../finetuneLLM/finetuned_models/samsumBad-7b-fp16-peft-seed-42"
+config = SafeLoraConfig(
+    base_model_path="meta-llama/Llama-2-7b-hf",
+    aligned_model_path="TheBloke/Llama-2-7B-Chat-fp16",
+    peft_model_path=peft_path,
+    device="cuda",
+    select_layers_type="threshold",
+    save_weights=True,
+)
+
+final_lora_weight = apply_safelora(config)
+
+save_file(
+    final_lora_weight,
+    f"{os.path.join('../finetuneLLM/finetuned_models/samsumBad-7b-fp16-peft-seed-42', 'adapter_model.safetensors')}",
+)
+
+model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-fp16")
+model = PeftModel.from_pretrained(model, peft_path)

src/peft/utils/loftq_utils.py

@@ -23,7 +23,7 @@
 
 import torch
 from huggingface_hub import snapshot_download
-from huggingface_hub.errors import HFValidationError, LocalEntryNotFoundError
+from huggingface_hub.errors import HFValidationError
 from safetensors import SafetensorError, safe_open
 from transformers.utils import cached_file
 from transformers.utils.hub import get_checkpoint_shard_files
@@ -267,26 +267,31 @@ class _SafetensorLoader:
 
     """
 
-    def __init__(self, peft_model, model_path):
+    def __init__(self, peft_model_or_model_id, model_path=None, local_files_only=True):
         if model_path is None:
-            try:
-                model_path = snapshot_download(peft_model.base_model.config._name_or_path, local_files_only=True)
-            except (AttributeError, HFValidationError) as exc:
-                raise ValueError(
-                    "The provided model does not appear to be a transformers model or is a local model. In this case, "
-                    "you must pass the model_path argument that points to the safetensors file."
-                ) from exc
-            except LocalEntryNotFoundError as exc:
-                raise ValueError(
-                    "The model.safetensors file must be present on disk, but it could not be found."
-                ) from exc
+            if isinstance(peft_model_or_model_id, str):
+                name_or_path = peft_model_or_model_id
+                base_model_prefix = None
+            else:
+                name_or_path = peft_model_or_model_id
+                base_model_prefix = getattr(peft_model_or_model_id.get_base_model(), "base_model_prefix", None)
+            if os.path.exists(name_or_path):
+                model_path = name_or_path
+            else:
+                try:
+                    model_path = snapshot_download(name_or_path, local_files_only=local_files_only)
+                except (AttributeError, HFValidationError):
+                    raise ValueError(
+                        "The provided model does not appear to be a transformers model or is a local model. In this case, "
+                        "you must pass the model_path argument that points to the safetensors file."
+                    )
 
         suffix = "model.safetensors"
         if not model_path.endswith(suffix):
             model_path = os.path.join(model_path, suffix)
 
         self.model_path = model_path
-        self.base_model_prefix = getattr(peft_model.get_base_model(), "base_model_prefix", None)
+        self.base_model_prefix = base_model_prefix
         self.prefix = "base_model.model."
         self.is_sharded = False
         self.weight_map = None

src/peft/utils/safelora.py

@@ -0,0 +1,227 @@
+# 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.
+# Reference paper: https://arxiv.org/abs/2405.16833
+
+
+import copy
+import os
+from dataclasses import dataclass, field
+from typing import Literal
+
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+from peft import PeftConfig
+
+from .loftq_utils import _SafetensorLoader as SafetensorLoader
+
+
+@dataclass
+class SafeLoraConfig:
+    """
+    This is the configuration class to store the configuration of a safeLora.
+
+
+    Args:
+
+    base_model_path (`str`): The path of the base model for obtaining the aligned matrix.
+
+    aligned_model_path (`str`): The path of the aligned model for obtaining the aligned matrix.
+
+    peft_model_path (`str`): The path of the LoRA weights and config.
+
+    select_layers_type (`Literal["threshold", "number"]`): How to select projection layers? options: [threshold, number]
+
+    threshold (`float`): The threshold of cosine similarity for selecting projected layers.
+
+    num_proj_layers (`int`): The number of projected layers.
+
+    device (`str`): Device that is used for SafeLoRA (cuda or cpu).
+
+    save_weights (`bool`): Replacing and saving SafeLoRA weights to the original LoRA file.
+
+    local_files_only (`bool`): Using for snapshot_download.
+
+    dtype (`torch.dtype`): Data type for model weights, e.g., torch.float32 or torch.bfloat16.
+
+    """
+
+    base_model_path: str = field(
+        default="meta-llama/Llama-2-7b-hf",
+        metadata={"help": "The path of the base model for obtaining the aligned matrix."},
+    )
+
+    aligned_model_path: str = field(
+        default="TheBloke/Llama-2-7B-Chat-fp16",
+        metadata={"help": "The path of the aligned model for obtaining the aligned matrix."},
+    )
+
+    peft_model_path: str = field(
+        default="LisaSchunke/llama-2-7b-peft-finetuned-20000-dataset",
+        metadata={"help": "The path of the LoRA wieghts and configs."},
+    )
+
+    select_layers_type: Literal["threshold", "number"] = field(
+        default="number",
+        metadata={"help": "How to select projection layers? options: [threshold, number]."},
+    )
+
+    threshold: float = field(
+        default=0.5,
+        metadata={"help": "The threshold of cosine similarity for selecting projected layers."},
+    )
+
+    num_proj_layers: int = field(
+        default=10,
+        metadata={"help": "The number of projected layers."},
+    )
+
+    device: str = field(
+        default="cuda",
+        metadata={"help": "Device is used in SafeLoRA. (cuda or cpu)"},
+    )
+
+    save_weights: bool = field(
+        default=True,
+        metadata={"help": "Replacing and saving SafeLoRA weights to the original LoRA file."},
+    )
+    local_files_only: bool = field(
+        default=False,
+        metadata={"help": "Using for snapshot_download."},
+    )
+
+    dtype: torch.dtype = field(
+        default=torch.bfloat16,
+        metadata={
+            "help": "Data type for model weights, e.g., torch.float32 or torch.bfloat16. If your device is CPU, you should use torch.float32."
+        },
+    )
+
+    def __post_init__(self):
+        if self.base_model_path is None:
+            raise ValueError("base_model_path cannot be None.")
+        if self.aligned_model_path is None:
+            raise ValueError("aligned_model_path cannot be None.")
+        if self.peft_model_path is None:
+            raise ValueError("peft_model_path cannot be None.")
+
+
+def get_aligned_matrix(base_model_path, aligned_model_path, peft_config, safelora_config):
+    """
+    Get projected matrix by following the config (target_modules) from the peft model.
+    The dimensions between the base model's weights and the aligned model's weights should be the same.
+    """
+    sl_align = SafetensorLoader(aligned_model_path, local_files_only=safelora_config.local_files_only)
+    sl_base = SafetensorLoader(base_model_path, local_files_only=safelora_config.local_files_only)
+
+    base_model_parameters = [
+        name for name in sl_base.weight_map.keys() if any(v in name for v in list(peft_config.target_modules))
+    ]
+
+    align_model_parameters = [
+        name for name in sl_align.weight_map.keys() if any(v in name for v in list(peft_config.target_modules))
+    ]
+    safety_vector = []
+    for name_base, name_align in zip(base_model_parameters, align_model_parameters):
+        if sl_base.get_tensor(name_base).shape != sl_align.get_tensor(name_align).shape:
+            raise ValueError(
+                "The dimensions of the base model's weight should be the same with the aligned model's weight."
+            )
+        if (sl_base.get_tensor(name_base) == sl_align.get_tensor(name_align)).all():
+            raise ValueError("The weights of the base Model and the aligned Model should be different.")
+        vec = sl_base.get_tensor(name_base) - sl_align.get_tensor(name_align)
+        vec = vec.to(safelora_config.dtype).to(safelora_config.device)
+        vec = torch.mm(vec, vec.t()) / torch.norm(vec)
+        safety_vector.append((vec).detach().cpu())
+    return safety_vector
+
+
+def project_weights(safelora_config, peft_weights, v):
+    ori_peft_weights = copy.deepcopy(peft_weights)
+    vars_names_LoRA_A = [name for name in peft_weights.keys() if "lora_A" in name]
+    vars_names_LoRA_B = [name for name in peft_weights.keys() if "lora_B" in name]
+    num_projected_layers = 0
+    dis = []
+    cos_total = []
+    for idx, (name_A, name_B) in enumerate(zip(vars_names_LoRA_A, vars_names_LoRA_B)):
+        A = ori_peft_weights[name_A]
+        P = v[idx].to(safelora_config.dtype).to(safelora_config.device)
+        W = torch.mm(P, ori_peft_weights[name_B])
+        fW = torch.mm(W, A)
+        ori = torch.mm(ori_peft_weights[name_B], A)
+        cos = torch.nn.functional.cosine_similarity(fW.reshape(1, -1), ori.reshape(1, -1))
+        cos_total.append(cos.item())
+        if cos <= safelora_config.threshold:
+            num_projected_layers += 1
+            peft_weights[name_B] = W
+        else:
+            peft_weights[name_B] = ori_peft_weights[name_B]
+
+        dist = 1 / (1 + torch.norm(peft_weights[name_B].reshape(1, -1) - W.reshape(1, -1)))
+
+        dis.append(dist.item())
+    return peft_weights, cos_total
+
+
+def apply_safelora(safelora_config: SafeLoraConfig):
+    """
+
+    The official code of Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models: https://arxiv.org/abs/2405.16833
+
+    After fine-tuning large language models (LLMs) using LoRA, the alignment of the resulting models may decrease.
+    Therefore, applying `apply_safelora()` is intended to help preserve the alignment of the final models.
+
+    It is important to note that the model weights of the aligned model and the base model must be of the same size.
+
+
+    Example:
+
+    from peft.utils.safelora import SafeLoraConfig, apply_safelora
+
+    config = SafeLoraConfig(base_model_path='meta-llama/Llama-2-7b-hf',\
+                            aligned_model_path='TheBloke/Llama-2-7B-Chat-fp16',
+                            peft_model_path = 'LisaSchunke/llama-2-7b-peft-finetuned-20000-dataset',
+                            device='cuda',
+                            select_layers_type='threshold',
+                            save_weights=True)
+
+    final_lora_weight = apply_safelora(config)
+
+    """
+
+    peft_config = PeftConfig.from_pretrained(safelora_config.peft_model_path)
+
+    projected_matrix = get_aligned_matrix(
+        safelora_config.base_model_path, safelora_config.aligned_model_path, peft_config, safelora_config
+    )
+
+    with safe_open(
+        f"{os.path.join(safelora_config.peft_model_path, 'adapter_model.safetensors')}",
+        framework="pt",
+        device=safelora_config.device,
+    ) as f:
+        peft_weights = {name: f.get_tensor(name).to(safelora_config.dtype) for name in f.keys()}
+    if safelora_config.select_layers_type == "threshold":
+        final_weights, _ = project_weights(safelora_config, peft_weights, projected_matrix)
+    elif safelora_config.select_layers_type == "number":
+        _, cos = project_weights(safelora_config, peft_weights, projected_matrix)
+        thrs = torch.sort(torch.Tensor(cos))[0][: safelora_config.num_proj_layers][-1]
+        safelora_config.threshold = thrs
+        final_weights, _ = project_weights(safelora_config, peft_weights, projected_matrix)
+
+    if safelora_config.save_weights:
+        save_file(final_weights, f"{os.path.join(safelora_config.peft_model_path, 'adapter_model.safetensors')}")
+
+    return final_weights