forked from p04798526/LLaMA-Factory-Mirror
support pissa
This commit is contained in:
hiyouga
parent
38b6b0f52e
commit
8c1046d78a
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@ -49,7 +49,7 @@ Choose your path:
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- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
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- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
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- **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.
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- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
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- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
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- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
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- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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@ -71,9 +71,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
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[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
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[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
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[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.
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[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
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@ -49,7 +49,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
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- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
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- **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。
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- **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。
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- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
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- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
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- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
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- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
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@ -71,9 +71,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
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## 更新日志
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[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
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[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)。
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[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。
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[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。
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[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)。
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@ -213,3 +213,9 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
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```bash
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bash examples/extras/fsdp_qlora/single_node.sh
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```
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#### PiSSA Fine-Tuning
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```bash
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llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
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```
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@ -213,3 +213,9 @@ llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
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```bash
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bash examples/extras/fsdp_qlora/single_node.sh
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```
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#### PiSSA 微调
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```bash
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llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
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```
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@ -0,0 +1,42 @@
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### model
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_target: all
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pissa_init: true
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pissa_iter: 4
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pissa_convert: true
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### dataset
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dataset: identity,alpaca_en_demo
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template: llama3
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cutoff_len: 1024
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max_samples: 1000
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overwrite_cache: true
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preprocessing_num_workers: 16
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### output
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output_dir: saves/llama3-8b/lora/sft
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logging_steps: 10
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save_steps: 500
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plot_loss: true
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overwrite_output_dir: true
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 1.0e-4
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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fp16: true
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ddp_timeout: 180000000
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### eval
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val_size: 0.1
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 500
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@ -1,7 +1,7 @@
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# coding=utf-8
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# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by HuggingFace's PEFT library.
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# This code is based on the HuggingFace's PEFT library.
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# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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@ -17,11 +17,9 @@
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# limitations under the License.
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import os
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from typing import TYPE_CHECKING, Optional
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from typing import TYPE_CHECKING
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import fire
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import torch
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import torch.nn as nn
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from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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@ -30,41 +28,20 @@ if TYPE_CHECKING:
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from transformers import PreTrainedModel
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class Shell(nn.Module):
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def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
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super().__init__()
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self.weight = nn.Parameter(weight, requires_grad=False)
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if bias is not None:
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self.bias = nn.Parameter(bias, requires_grad=False)
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def unwrap_model(model: nn.Module, pattern=".base_layer") -> None:
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for name in {k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k}:
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parent_name = ".".join(name.split(".")[:-1])
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child_name = name.split(".")[-1]
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parent_module = model.get_submodule(parent_name)
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child_module = getattr(parent_module, child_name)
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base_layer = getattr(child_module, "base_layer")
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weight = getattr(base_layer, "weight", None)
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bias = getattr(base_layer, "bias", None)
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setattr(parent_module, child_name, Shell(weight, bias))
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print("Model unwrapped.")
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def quantize_loftq(
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model_name_or_path: str,
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save_dir: str,
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loftq_bits: Optional[int] = 4,
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loftq_iter: Optional[int] = 1,
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lora_alpha: Optional[int] = None,
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lora_rank: Optional[int] = 16,
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lora_target: Optional[str] = "q_proj,v_proj",
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save_safetensors: Optional[bool] = False,
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output_dir: str,
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loftq_bits: int = 4,
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loftq_iter: int = 4,
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lora_alpha: int = None,
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lora_rank: int = 16,
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lora_dropout: float = 0,
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lora_target: str = "q_proj,v_proj",
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save_safetensors: bool = True,
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):
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r"""
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Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
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Usage: python loftq_init.py --model_name_or_path path_to_model --save_dir output_dir
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Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
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"""
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
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@ -74,25 +51,34 @@ def quantize_loftq(
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inference_mode=True,
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r=lora_rank,
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lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
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lora_dropout=0.1,
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lora_dropout=lora_dropout,
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target_modules=[name.strip() for name in lora_target.split(",")],
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init_lora_weights="loftq",
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loftq_config=loftq_config,
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)
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# Init LoftQ model
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lora_model = get_peft_model(model, lora_config)
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base_model: "PreTrainedModel" = lora_model.get_base_model()
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print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
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peft_model = get_peft_model(model, lora_config)
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loftq_dir = os.path.join(output_dir, "loftq_init")
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# Save LoftQ model
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setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir)
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setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True)
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lora_model.save_pretrained(os.path.join(save_dir, "adapters"), safe_serialization=save_safetensors)
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setattr(peft_model.peft_config["default"], "base_model_name_or_path", output_dir)
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setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply loftq again
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peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
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print("Adapter weights saved in {}".format(loftq_dir))
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# Save base model
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unwrap_model(base_model)
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base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
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tokenizer.save_pretrained(save_dir)
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base_model: "PreTrainedModel" = peft_model.unload()
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base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
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tokenizer.save_pretrained(output_dir)
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print("Model weights saved in {}".format(output_dir))
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print("Fine-tune this model with:")
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print("model_name_or_path: {}".format(output_dir))
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print("adapter_name_or_path: {}".format(loftq_dir))
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print("finetuning_type: lora")
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print("quantization_bit: {}".format(loftq_bits))
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if __name__ == "__main__":
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@ -0,0 +1,79 @@
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# coding=utf-8
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# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is based on the HuggingFace's PEFT library.
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# https://github.com/huggingface/peft/blob/v0.11.0/examples/pissa_finetuning/preprocess.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import TYPE_CHECKING
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import fire
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from peft import LoraConfig, TaskType, get_peft_model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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def quantize_pissa(
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model_name_or_path: str,
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output_dir: str,
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pissa_iter: int = 4,
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lora_alpha: int = None,
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lora_rank: int = 16,
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lora_dropout: float = 0,
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lora_target: str = "q_proj,v_proj",
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save_safetensors: bool = True,
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):
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r"""
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Initializes LoRA weights with Principal Singular values and Singular vectors Adaptation (PiSSA)
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Usage: python pissa_init.py --model_name_or_path path_to_model --output_dir output_dir
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"""
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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r=lora_rank,
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lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
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lora_dropout=lora_dropout,
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target_modules=[name.strip() for name in lora_target.split(",")],
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init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter)
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)
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# Init PiSSA model
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peft_model = get_peft_model(model, lora_config)
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pissa_dir = os.path.join(output_dir, "pissa_init")
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# Save PiSSA model
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setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply pissa again
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peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
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print("Adapter weights saved in {}".format(pissa_dir))
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# Save base model
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base_model: "PreTrainedModel" = peft_model.unload()
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base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
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tokenizer.save_pretrained(output_dir)
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print("Model weights saved in {}".format(output_dir))
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print("Fine-tune this model with:")
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print("model_name_or_path: {}".format(output_dir))
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print("adapter_name_or_path: {}".format(pissa_dir))
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print("finetuning_type: lora")
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print("pissa_convert: true")
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if __name__ == "__main__":
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fire.Fire(quantize_pissa)
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@ -108,6 +108,18 @@ class LoraArguments:
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default=False,
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metadata={"help": "Whether or not to use the weight-decomposed lora method (DoRA)."},
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)
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pissa_init: bool = field(
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default=False,
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metadata={"help": "Whether or not to initialize a PiSSA adapter."},
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)
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pissa_iter: int = field(
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default=4,
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metadata={"help": "The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it."},
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)
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pissa_convert: bool = field(
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default=False,
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metadata={"help": "Whether or not to convert the PiSSA adapter to a normal LoRA adapter."},
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)
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create_new_adapter: bool = field(
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default=False,
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metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
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@ -340,7 +352,7 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
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self.additional_target: Optional[List[str]] = split_arg(self.additional_target)
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self.galore_target: List[str] = split_arg(self.galore_target)
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self.freeze_vision_tower = self.freeze_vision_tower or self.train_mm_proj_only
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self.use_ref_model = self.pref_loss not in ["orpo", "simpo"]
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self.use_ref_model = (self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"])
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assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
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assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
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@ -367,5 +379,11 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
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if self.loraplus_lr_ratio is not None and self.finetuning_type != "lora":
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raise ValueError("`loraplus_lr_ratio` is only valid for LoRA training.")
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if self.pissa_convert and self.finetuning_type != "lora":
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raise ValueError("`pissa_convert` is only valid for LoRA training.")
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if self.pissa_convert and (self.stage in ["rm", "ppo", "kto"] or self.use_ref_model):
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raise ValueError("Cannot use PiSSA for current training stage.")
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if self.train_mm_proj_only and self.finetuning_type != "full":
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raise ValueError("`train_mm_proj_only` is only valid for full training.")
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@ -1,6 +1,6 @@
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# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by HuggingFace's transformers library.
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# 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(
|
||||
|
|
|
|||
|
|
@ -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.")
|
||||
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
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:
|
||||
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
||||
getattr(self.processor, "image_processor").save_pretrained(output_dir)
|
||||
|
||||
def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
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:
|
||||
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
||||
getattr(self.processor, "image_processor").save_pretrained(output_dir)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
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:
|
||||
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
||||
getattr(self.processor, "image_processor").save_pretrained(output_dir)
|
||||
|
||||
def prediction_step(
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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_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,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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)",
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
Loading…
Reference in New Issue