support pissa

README.md

@@ -49,7 +49,7 @@ Choose your path:
 - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
 - **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
-- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
+- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
 - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
 - **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
@@ -71,9 +71,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
-[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
+[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
 
-[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
+[24/06/07] We supported fine-tuning the **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** and **[GLM-4](https://github.com/THUDM/GLM-4)** models.
 
 [24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 

README_zh.md

@@ -49,7 +49,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
 - **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
 - **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
-- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
+- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
 - **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
 - **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
 - **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
@@ -71,9 +71,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 ## 更新日志
 
-[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
+[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。
 
-[24/06/05] 我们支持了 **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** 模型的微调。
+[24/06/07] 我们支持了 **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** 和 **[GLM-4](https://github.com/THUDM/GLM-4)** 模型的微调。
 
 [24/05/26] 我们支持了 **[SimPO](https://arxiv.org/abs/2405.14734)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
 

examples/README.md

@@ -213,3 +213,9 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/single_node.sh
 ```
+
+#### PiSSA Fine-Tuning
+
+```bash
+llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
+```

examples/README_zh.md

@@ -213,3 +213,9 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```bash
 bash examples/extras/fsdp_qlora/single_node.sh
 ```
+
+#### PiSSA 微调
+
+```bash
+llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
+```

examples/extras/pissa/llama3_lora_sft.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+pissa_init: true
+pissa_iter: 4
+pissa_convert: true
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+ddp_timeout: 180000000
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

scripts/loftq_init.py

@@ -1,7 +1,7 @@
 # coding=utf-8
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by HuggingFace's PEFT library.
+# This code is based on the HuggingFace's PEFT library.
 # https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,11 +17,9 @@
 # limitations under the License.
 
 import os
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import fire
-import torch
-import torch.nn as nn
 from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -30,41 +28,20 @@ if TYPE_CHECKING:
     from transformers import PreTrainedModel
 
 
-class Shell(nn.Module):
-    def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        super().__init__()
-        self.weight = nn.Parameter(weight, requires_grad=False)
-        if bias is not None:
-            self.bias = nn.Parameter(bias, requires_grad=False)
-
-
-def unwrap_model(model: nn.Module, pattern=".base_layer") -> None:
-    for name in {k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k}:
-        parent_name = ".".join(name.split(".")[:-1])
-        child_name = name.split(".")[-1]
-        parent_module = model.get_submodule(parent_name)
-        child_module = getattr(parent_module, child_name)
-        base_layer = getattr(child_module, "base_layer")
-        weight = getattr(base_layer, "weight", None)
-        bias = getattr(base_layer, "bias", None)
-        setattr(parent_module, child_name, Shell(weight, bias))
-
-    print("Model unwrapped.")
-
-
 def quantize_loftq(
     model_name_or_path: str,
-    save_dir: str,
-    loftq_bits: Optional[int] = 4,
-    loftq_iter: Optional[int] = 1,
-    lora_alpha: Optional[int] = None,
-    lora_rank: Optional[int] = 16,
-    lora_target: Optional[str] = "q_proj,v_proj",
-    save_safetensors: Optional[bool] = False,
+    output_dir: str,
+    loftq_bits: int = 4,
+    loftq_iter: int = 4,
+    lora_alpha: int = None,
+    lora_rank: int = 16,
+    lora_dropout: float = 0,
+    lora_target: str = "q_proj,v_proj",
+    save_safetensors: bool = True,
 ):
     r"""
     Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
-    Usage: python loftq_init.py --model_name_or_path path_to_model --save_dir output_dir
+    Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
     """
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
@@ -74,25 +51,34 @@ def quantize_loftq(
         inference_mode=True,
         r=lora_rank,
         lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
-        lora_dropout=0.1,
+        lora_dropout=lora_dropout,
         target_modules=[name.strip() for name in lora_target.split(",")],
         init_lora_weights="loftq",
         loftq_config=loftq_config,
     )
 
     # Init LoftQ model
-    lora_model = get_peft_model(model, lora_config)
-    base_model: "PreTrainedModel" = lora_model.get_base_model()
+    print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
+    peft_model = get_peft_model(model, lora_config)
+    loftq_dir = os.path.join(output_dir, "loftq_init")
 
     # Save LoftQ model
-    setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir)
-    setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True)
-    lora_model.save_pretrained(os.path.join(save_dir, "adapters"), safe_serialization=save_safetensors)
+    setattr(peft_model.peft_config["default"], "base_model_name_or_path", output_dir)
+    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply loftq again
+    peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
+    print("Adapter weights saved in {}".format(loftq_dir))
 
     # Save base model
-    unwrap_model(base_model)
-    base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
-    tokenizer.save_pretrained(save_dir)
+    base_model: "PreTrainedModel" = peft_model.unload()
+    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
+    tokenizer.save_pretrained(output_dir)
+    print("Model weights saved in {}".format(output_dir))
+
+    print("Fine-tune this model with:")
+    print("model_name_or_path: {}".format(output_dir))
+    print("adapter_name_or_path: {}".format(loftq_dir))
+    print("finetuning_type: lora")
+    print("quantization_bit: {}".format(loftq_bits))
 
 
 if __name__ == "__main__":

scripts/pissa_init.py

@@ -0,0 +1,79 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is based on the HuggingFace's PEFT library.
+# https://github.com/huggingface/peft/blob/v0.11.0/examples/pissa_finetuning/preprocess.py
+#
+# 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 typing import TYPE_CHECKING
+
+import fire
+from peft import LoraConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+
+def quantize_pissa(
+    model_name_or_path: str,
+    output_dir: str,
+    pissa_iter: int = 4,
+    lora_alpha: int = None,
+    lora_rank: int = 16,
+    lora_dropout: float = 0,
+    lora_target: str = "q_proj,v_proj",
+    save_safetensors: bool = True,
+):
+    r"""
+    Initializes LoRA weights with Principal Singular values and Singular vectors Adaptation (PiSSA)
+    Usage: python pissa_init.py --model_name_or_path path_to_model --output_dir output_dir
+    """
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
+    lora_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM,
+        r=lora_rank,
+        lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
+        lora_dropout=lora_dropout,
+        target_modules=[name.strip() for name in lora_target.split(",")],
+        init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter)
+    )
+
+    # Init PiSSA model
+    peft_model = get_peft_model(model, lora_config)
+    pissa_dir = os.path.join(output_dir, "pissa_init")
+
+    # Save PiSSA model
+    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply pissa again
+    peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
+    print("Adapter weights saved in {}".format(pissa_dir))
+
+    # Save base model
+    base_model: "PreTrainedModel" = peft_model.unload()
+    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
+    tokenizer.save_pretrained(output_dir)
+    print("Model weights saved in {}".format(output_dir))
+
+    print("Fine-tune this model with:")
+    print("model_name_or_path: {}".format(output_dir))
+    print("adapter_name_or_path: {}".format(pissa_dir))
+    print("finetuning_type: lora")
+    print("pissa_convert: true")
+
+
+if __name__ == "__main__":
+    fire.Fire(quantize_pissa)

src/llamafactory/hparams/finetuning_args.py

@@ -108,6 +108,18 @@ class LoraArguments:
         default=False,
         metadata={"help": "Whether or not to use the weight-decomposed lora method (DoRA)."},
     )
+    pissa_init: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to initialize a PiSSA adapter."},
+    )
+    pissa_iter: int = field(
+        default=4,
+        metadata={"help": "The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it."},
+    )
+    pissa_convert: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to convert the PiSSA adapter to a normal LoRA adapter."},
+    )
     create_new_adapter: bool = field(
         default=False,
         metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
@@ -340,7 +352,7 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
         self.additional_target: Optional[List[str]] = split_arg(self.additional_target)
         self.galore_target: List[str] = split_arg(self.galore_target)
         self.freeze_vision_tower = self.freeze_vision_tower or self.train_mm_proj_only
-        self.use_ref_model = self.pref_loss not in ["orpo", "simpo"]
+        self.use_ref_model = (self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"])
 
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
         assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
@@ -367,5 +379,11 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
         if self.loraplus_lr_ratio is not None and self.finetuning_type != "lora":
             raise ValueError("`loraplus_lr_ratio` is only valid for LoRA training.")
 
+        if self.pissa_convert and self.finetuning_type != "lora":
+            raise ValueError("`pissa_convert` is only valid for LoRA training.")
+
+        if self.pissa_convert and (self.stage in ["rm", "ppo", "kto"] or self.use_ref_model):
+            raise ValueError("Cannot use PiSSA for current training stage.")
+
         if self.train_mm_proj_only and self.finetuning_type != "full":
             raise ValueError("`train_mm_proj_only` is only valid for full training.")

src/llamafactory/hparams/model_args.py

@@ -1,6 +1,6 @@
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by HuggingFace's transformers library.
+# This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -45,6 +45,10 @@ class ModelArguments:
             )
         },
     )
+    adapter_folder: Optional[str] = field(
+        default=None,
+        metadata={"help": "The folder containing the adapter weights to load."},
+    )
     cache_dir: Optional[str] = field(
         default=None,
         metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
@@ -150,7 +154,7 @@ class ModelArguments:
         metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
     )
     vllm_max_lora_rank: int = field(
-        default=8,
+        default=32,
         metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
     )
     offload_folder: str = field(

src/llamafactory/hparams/parser.py

@@ -1,6 +1,6 @@
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by HuggingFace's transformers library.
+# This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -90,6 +90,9 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
         if finetuning_args.finetuning_type != "lora":
             raise ValueError("Quantization is only compatible with the LoRA method.")
 
+        if finetuning_args.use_pissa:
+            raise ValueError("Please use scripts/pissa_init.py for quantized PiSSA.")
+
         if model_args.resize_vocab:
             raise ValueError("Cannot resize embedding layers of a quantized model.")
 

src/llamafactory/model/adapter.py

@@ -179,8 +179,16 @@ def _setup_lora_tuning(
         else:
             adapter_to_merge = model_args.adapter_name_or_path
 
+        init_kwargs = {
+            "subfolder": model_args.adapter_folder,
+            "offload_folder": model_args.offload_folder,
+            "cache_dir": model_args.cache_dir,
+            "revision": model_args.model_revision,
+            "token": model_args.hf_hub_token,
+        }
+
         for adapter in adapter_to_merge:
-            model: "LoraModel" = PeftModel.from_pretrained(model, adapter, offload_folder=model_args.offload_folder)
+            model: "LoraModel" = PeftModel.from_pretrained(model, adapter, **init_kwargs)
             model = model.merge_and_unload()
 
         if len(adapter_to_merge) > 0:
@@ -190,12 +198,7 @@ def _setup_lora_tuning(
             if model_args.use_unsloth:
                 model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
             else:
-                model = PeftModel.from_pretrained(
-                    model,
-                    adapter_to_resume,
-                    is_trainable=is_trainable,
-                    offload_folder=model_args.offload_folder,
-                )
+                model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
 
         logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
 
@@ -242,6 +245,14 @@ def _setup_lora_tuning(
         if model_args.use_unsloth:
             model = get_unsloth_peft_model(model, model_args, peft_kwargs)
         else:
+            if finetuning_args.pissa_init:
+                if finetuning_args.pissa_iter == -1:
+                    logger.info("Using PiSSA initialization.")
+                    peft_kwargs["init_lora_weights"] = "pissa"
+                else:
+                    logger.info("Using PiSSA initialization with FSVD steps {}.".format(finetuning_args.pissa_iter))
+                    peft_kwargs["init_lora_weights"] = "pissa_niter_{}".format(finetuning_args.pissa_iter)
+
             lora_config = LoraConfig(
                 task_type=TaskType.CAUSAL_LM,
                 inference_mode=False,

src/llamafactory/train/dpo/trainer.py

@@ -1,6 +1,6 @@
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by HuggingFace's TRL library.
+# This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/dpo_trainer.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import warnings
 from collections import defaultdict
 from contextlib import nullcontext
@@ -28,7 +29,7 @@ from trl import DPOTrainer
 from trl.trainer import disable_dropout_in_model
 
 from ...extras.constants import IGNORE_INDEX
-from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps
+from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler, get_batch_logps
 
 
 if TYPE_CHECKING:
@@ -91,6 +92,9 @@ class CustomDPOTrainer(DPOTrainer):
                 self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
                 self.ref_model.eval()
 
+        if finetuning_args.pissa_convert:
+            self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
+
         if finetuning_args.use_badam:
             from badam import clip_grad_norm_for_sparse_tensor
 
@@ -109,8 +113,11 @@ class CustomDPOTrainer(DPOTrainer):
 
     def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
         super()._save(output_dir, state_dict)
-        if self.processor is not None:
         output_dir = output_dir if output_dir is not None else self.args.output_dir
+        if self.finetuning_args.pissa_convert:
+            convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
+
+        if self.processor is not None:
             getattr(self.processor, "image_processor").save_pretrained(output_dir)
 
     def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":

src/llamafactory/train/pt/trainer.py

@@ -12,13 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 from types import MethodType
 from typing import TYPE_CHECKING, Dict, Optional
 
 from transformers import Trainer
 
 from ...extras.logging import get_logger
-from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
+from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
@@ -42,6 +43,10 @@ class CustomTrainer(Trainer):
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
         self.processor = processor
+
+        if finetuning_args.pissa_convert:
+            self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
+
         if finetuning_args.use_badam:
             from badam import clip_grad_norm_for_sparse_tensor
 
@@ -60,6 +65,9 @@ class CustomTrainer(Trainer):
 
     def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
         super()._save(output_dir, state_dict)
-        if self.processor is not None:
         output_dir = output_dir if output_dir is not None else self.args.output_dir
+        if self.finetuning_args.pissa_convert:
+            convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
+
+        if self.processor is not None:
             getattr(self.processor, "image_processor").save_pretrained(output_dir)

src/llamafactory/train/sft/trainer.py

@@ -1,6 +1,6 @@
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by HuggingFace's transformers library.
+# This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -26,7 +26,7 @@ from transformers import Seq2SeqTrainer
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
-from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
+from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
@@ -51,6 +51,10 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
         self.processor = processor
+
+        if finetuning_args.pissa_convert:
+            self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
+
         if finetuning_args.use_badam:
             from badam import clip_grad_norm_for_sparse_tensor
 
@@ -69,8 +73,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
 
     def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
         super()._save(output_dir, state_dict)
-        if self.processor is not None:
         output_dir = output_dir if output_dir is not None else self.args.output_dir
+        if self.finetuning_args.pissa_convert:
+            convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
+
+        if self.processor is not None:
             getattr(self.processor, "image_processor").save_pretrained(output_dir)
 
     def prediction_step(

src/llamafactory/train/trainer_utils.py

@@ -1,9 +1,9 @@
 # Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
 #
-# This code is inspired by the GaLore's implementation: https://github.com/jiaweizzhao/GaLore
-# and the LoRA+'s implementation: https://github.com/nikhil-ghosh-berkeley/loraplus
-# and the BAdam's implementation: https://github.com/Ledzy/BAdam
-# and the TRL's implementation: https://github.com/huggingface/trl
+# This code is inspired by the original GaLore's implementation: https://github.com/jiaweizzhao/GaLore
+# and the original LoRA+'s implementation: https://github.com/nikhil-ghosh-berkeley/loraplus
+# and the original BAdam's implementation: https://github.com/Ledzy/BAdam
+# and the HuggingFace's TRL library: https://github.com/huggingface/trl
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,9 +17,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
+from peft import PeftModel
 from transformers import Trainer
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
@@ -37,6 +39,7 @@ if is_galore_available():
 
 
 if TYPE_CHECKING:
+    from accelerate import Accelerator
     from transformers import PreTrainedModel, Seq2SeqTrainingArguments
     from trl import AutoModelForCausalLMWithValueHead
 
@@ -171,6 +174,49 @@ def create_reward_model(
         return reward_model
 
 
+def convert_pissa_adapter(
+    output_dir: str,
+    state_dict: Dict[str, "torch.Tensor"],
+    accelerator: "Accelerator",
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+) -> None:
+    r"""
+    Converts the PiSSA adapter to a LoRA adapter.
+    """
+    pissa_init_dir = os.path.join(training_args.output_dir, "pissa_init")
+    pissa_backup_dir = os.path.join(output_dir, "pissa_backup")
+    if output_dir == pissa_init_dir:
+        logger.info("Initial PiSSA adatper will be saved at: {}.".format(pissa_init_dir))
+        unwrapped_model = accelerator.unwrap_model(model)
+        if isinstance(unwrapped_model, PeftModel):
+            init_lora_weights = getattr(unwrapped_model.peft_config["default"], "init_lora_weights")
+            setattr(unwrapped_model.peft_config["default"], "init_lora_weights", True)
+            unwrapped_model.save_pretrained(
+                output_dir,
+                state_dict=state_dict,
+                safe_serialization=training_args.save_safetensors,
+            )
+            setattr(unwrapped_model.peft_config["default"], "init_lora_weights", init_lora_weights)
+    elif output_dir == training_args.output_dir:  # at the end of training
+        logger.info("Converted PiSSA adapter will be saved at: {}.".format(output_dir))
+        unwrapped_model = accelerator.unwrap_model(model)
+        if isinstance(unwrapped_model, PeftModel):  # backup the pissa adapter for further use
+            unwrapped_model.save_pretrained(
+                pissa_backup_dir,
+                state_dict=state_dict,
+                safe_serialization=training_args.save_safetensors,
+            )
+            unwrapped_model.save_pretrained(
+                output_dir,
+                state_dict=state_dict,
+                safe_serialization=training_args.save_safetensors,
+                convert_pissa_to_lora=pissa_init_dir,
+            )
+            unwrapped_model.load_adapter(pissa_backup_dir, "default", is_trainable=True)
+            unwrapped_model.set_adapter("default")
+
+
 def _get_decay_parameter_names(model: "PreTrainedModel") -> List[str]:
     r"""
     Returns a list of names of parameters with weight decay. (weights in non-layernorm layers)

src/llamafactory/webui/components/train.py

@@ -163,10 +163,9 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             create_new_adapter = gr.Checkbox()
 
         with gr.Row():
-            with gr.Column(scale=1):
             use_rslora = gr.Checkbox()
             use_dora = gr.Checkbox()
-
+            use_pissa = gr.Checkbox()
             lora_target = gr.Textbox(scale=2)
             additional_target = gr.Textbox(scale=2)
 
@@ -179,6 +178,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             create_new_adapter,
             use_rslora,
             use_dora,
+            use_pissa,
             lora_target,
             additional_target,
         }
@@ -193,6 +193,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             create_new_adapter=create_new_adapter,
             use_rslora=use_rslora,
             use_dora=use_dora,
+            use_pissa=use_pissa,
             lora_target=lora_target,
             additional_target=additional_target,
         )

src/llamafactory/webui/locales.py

@@ -732,6 +732,20 @@ LOCALES = {
             "info": "使用权重分解的 LoRA。",
         },
     },
+    "use_pissa": {
+        "en": {
+            "label": "Use PiSSA",
+            "info": "Use PiSSA method.",
+        },
+        "ru": {
+            "label": "используйте PiSSA",
+            "info": "Используйте метод PiSSA.",
+        },
+        "zh": {
+            "label": "使用 PiSSA",
+            "info": "使用 PiSSA 方法。",
+        },
+    },
     "lora_target": {
         "en": {
             "label": "LoRA modules (optional)",

src/llamafactory/webui/runner.py

@@ -173,6 +173,8 @@ class Runner:
             args["create_new_adapter"] = get("train.create_new_adapter")
             args["use_rslora"] = get("train.use_rslora")
             args["use_dora"] = get("train.use_dora")
+            args["pissa_init"] = get("train.use_pissa")
+            args["pissa_convert"] = get("train.use_pissa")
             args["lora_target"] = get("train.lora_target") or "all"
             args["additional_target"] = get("train.additional_target") or None
 

tests/model/test_pissa.py

@@ -0,0 +1,90 @@
+# Copyright 2024 the LlamaFactory 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
+
+import torch
+from peft import LoraModel, PeftModel
+from transformers import AutoModelForCausalLM
+
+from llamafactory.extras.misc import get_current_device
+from llamafactory.hparams import get_infer_args, get_train_args
+from llamafactory.model import load_model, load_tokenizer
+
+
+TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
+
+TINY_LLAMA_PISSA = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")
+
+TRAIN_ARGS = {
+    "model_name_or_path": TINY_LLAMA,
+    "stage": "sft",
+    "do_train": True,
+    "finetuning_type": "lora",
+    "pissa_init": True,
+    "pissa_iter": -1,
+    "dataset": "llamafactory/tiny-supervised-dataset",
+    "dataset_dir": "ONLINE",
+    "template": "llama3",
+    "cutoff_len": 1024,
+    "overwrite_cache": True,
+    "output_dir": "dummy_dir",
+    "overwrite_output_dir": True,
+    "fp16": True,
+}
+
+INFER_ARGS = {
+    "model_name_or_path": TINY_LLAMA_PISSA,
+    "adapter_name_or_path": TINY_LLAMA_PISSA,
+    "adapter_folder": "pissa_init",
+    "finetuning_type": "lora",
+    "template": "llama3",
+    "infer_dtype": "float16",
+}
+
+
+def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
+    state_dict_a = model_a.state_dict()
+    state_dict_b = model_b.state_dict()
+    assert set(state_dict_a.keys()) == set(state_dict_b.keys())
+    for name in state_dict_a.keys():
+        assert torch.allclose(state_dict_a[name], state_dict_b[name])
+
+
+def test_pissa_init():
+    model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+
+    base_model = AutoModelForCausalLM.from_pretrained(
+        TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
+    )
+    ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init", is_trainable=True)
+    for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
+        param.data = param.data.to(torch.float32)
+
+    compare_model(model, ref_model)
+
+
+def test_pissa_inference():
+    model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
+
+    base_model = AutoModelForCausalLM.from_pretrained(
+        TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
+    )
+    ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init")
+    ref_model = ref_model.merge_and_unload()
+    compare_model(model, ref_model)