├── .github └── todo.yaml ├── .gitignore ├── LICENSE ├── README.md ├── assets ├── 89F5EE60-13D9-416B-B395-8774B4350509.webp └── qrcode_1731259533808.jpg ├── benchmark └── test.py ├── config ├── config.yaml ├── loss │ ├── cdpo.yaml │ ├── csft.yaml │ ├── dpo-sigmoid.yaml │ ├── dpo.yaml │ ├── fdpo-kl.yaml │ ├── fpo.yaml │ ├── kto-logsigmoid.yaml │ ├── kto-simple.yaml │ ├── kto-surprisal.yaml │ ├── kto-zero.yaml │ ├── kto.yaml │ ├── orpo.yaml │ ├── ppo.yaml │ ├── sft.yaml │ ├── simpo.yaml │ ├── slic.yaml │ ├── tdpo1.yaml │ └── tdpo2.yaml └── model │ ├── base_model.yaml │ ├── gemma-2-2b.yaml │ ├── gemma-2-9b.yaml │ ├── llama13b.yaml │ ├── llama30b.yaml │ ├── llama65b.yaml │ ├── llama7b.yaml │ ├── mistral7b.yaml │ ├── mistral7b_instruct.yaml │ ├── mistral7b_sft_beta.yaml │ ├── pythia1-4b.yaml │ ├── pythia12-0b.yaml │ ├── pythia2-8b.yaml │ ├── pythia6-9b.yaml │ ├── qwen-2-1.5b.yaml │ └── zephyr-sft-beta.yaml ├── data └── dataloader.py ├── debug.py ├── environment.yaml ├── feature_alignment ├── __init__.py ├── compare.py ├── eval.py ├── feature_map.py ├── model │ ├── dpo.py │ ├── fpo.py │ ├── model.py │ ├── sft.py │ ├── simpo.py │ └── tdpo.py ├── models.py ├── push.py ├── sae │ └── jump_relu_sae.py ├── trainers.py ├── transformers_model │ └── modeling_gemma2.py ├── utils │ ├── __init__.py │ ├── callbacks.py │ └── util.py └── visualize.py ├── requirements.txt ├── run.sh ├── sample.py └── train.py /.github/todo.yaml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/.github/todo.yaml -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | outputs 3 | results 4 | .vscode 5 | .cache 6 | test 7 | wandb 8 | cache 9 | *.json 10 | weighted_alpaca_eval_gpt4_turbo 11 | samples 12 | alpaca_eval 13 | figures 14 | *.jsonl 15 | *.png 16 | *.sh 17 | *.csv 18 | debug.py -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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4 | Llama Image 5 |

FeatureAlignment

6 |
7 | 8 |

9 | License 10 | Static Badge 11 | Static Badge 12 | Static Badge 13 | 14 |

15 |

16 | FeatureAlignment is a tool designed to enhance the alignment of large language models (LLMs) by leveraging the power of interpretability. The core idea behind this repository is to align models through meaningful features. Traditional alignment methods in the past focused on the explicit outputs of LLMs, such as logits. 17 | 18 | In contrast, we are more interested in leveraging the inherent interpretable features of LLMs for alignment. 19 |

20 | 21 | $$ 22 | \text{FeatureAlignment} = \text{Alignment} (\text{e.g. DPO}) + \text{Mechanistic Interpretability} (\text{e.g. SAE}) 23 | $$ 24 | 25 | ## 🎯 Key Highlights 26 | - Compatible with [Transformer Lens](https://github.com/TransformerLensOrg/TransformerLens), [SAE Lens](https://github.com/jbloomAus/SAELens) and [Transformers](https://github.com/huggingface/transformers). 27 | - Support multiple alignment methods e.g. DPO, SimPO, TDPO, ORPO. 28 | - Pytorch Lightning + Hydra + WandB / Neupton for easy training. 29 | - Template for customizing alignment methods. 30 | 31 | > [!REMINDER] 32 | > This repository is still in a stage of rapid updates and development, and we welcome any pull requests and suggestions. If you would like to add your method to this repository, please feel free to contact us directly. 33 | 34 | ## Supports 35 | 36 |
37 | 38 | ### Alignment Methods Supported 39 | 40 | | Method | Time | Paper | Official Code | Support | 41 | |--------|---------|----------------------------------|------------------------------------------------|---------| 42 | | DPO | 2023.05 | https://arxiv.org/abs/2305.18290 | [code](https://github.com/junkangwu/alpha-DPO) | ✅ | 43 | | KTO | 2024.02 | https://arxiv.org/abs/2402.01306 | [code](https://github.com/junkangwu/alpha-DPO) | TODO | 44 | | ORPO | 2024.03 | https://arxiv.org/abs/2403.07691 | [code](https://github.com/junkangwu/alpha-DPO) | TODO | 45 | | TDPO | 2024.04 | https://arxiv.org/abs/2404.11999 | [code](https://github.com/junkangwu/alpha-DPO) | ✅ | 46 | | SimPO | 2024.05 | https://arxiv.org/abs/2405.14734 | [code](https://github.com/junkangwu/alpha-DPO) | ✅ | 47 | | α-DPO | 2024.10 | https://arxiv.org/abs/2410.10148 | [code](https://github.com/junkangwu/alpha-DPO) | TODO | 48 | | FPO | 2024.10 | - | [code](https://github.com/junkangwu/alpha-DPO) | ✅ | 49 | 50 | ### SAE Models Supported 51 | | Model | Type | Paper / blog | Code | Huggingface | Support | 52 | |-----------------------|:-----------:|:----------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------:|:-------:| 53 | | Gemma-Scope (Gemma-2) | Base / Chat | [ArXiv](https://arxiv.org/abs/2408.05147) | [JumpReLU](https://github.com/erichson/JumpReLU) | [Link](https://huggingface.co/google/gemma-scope) | ✅ | 54 | | LLaMA-Scope (LLaMA-3) | Base | [ArXiv](https://arxiv.org/abs/2410.20526) | - | [Link](https://huggingface.co/fnlp/Llama-Scope) | - | 55 | | Qwen 1.5 0.5B | Base / Chat | [Alignment Forum](https://www.alignmentforum.org/posts/fmwk6qxrpW8d4jvbd/saes-usually-transfer-between-base-and-chat-models) | [SAE Transfer](https://github.com/ckkissane/sae-transfer) | - | - | 56 | | Mistral-7B | Base / Chat | [Alignment Forum](https://www.alignmentforum.org/posts/fmwk6qxrpW8d4jvbd/saes-usually-transfer-between-base-and-chat-models) | [SAE Transfer](https://github.com/ckkissane/sae-transfer) | - | - | 57 | | LLaMA-3-8B | Base | - | [EleutherAI SAE](https://github.com/EleutherAI/sae) | [Link](https://huggingface.co/EleutherAI/sae-llama-3-8b-32x) | - | 58 | 59 |
60 | 61 | ## ⚡ Quick Start 62 | 63 | ### 1. Setting Up the Environment 64 | 65 | First things first, you'll need to set up the environment. 66 | 67 | ```bash 68 | conda env create -f environment.yml 69 | conda activate halos 70 | ``` 71 | 72 | Problems during installation? Try this manual setup: 73 | 74 | ```bash 75 | conda create -n fpo python=3.10.12 76 | pip3 install numpy==1.24.3 ninja==1.11.1 packaging==23.1 77 | conda install pytorch==2.1.1 pytorch-cuda=12.1 -c pytorch -c nvidia 78 | pip3 install flash-attn==2.3.3 transformers==4.35.2 datasets hydra-core==1.3.2 wandb==0.15.3 openai==1.6.1 accelerate==0.21.0 tensor-parallel==1.2.4 79 | ``` 80 | 81 | ### 2. Project Structure 82 | 83 | Before starting training or testing, let's go over the overall structure of the project files. 84 | 85 | ``` 86 | benchmark 87 | config 88 | data 89 | scripts 90 | feature_alignment 91 | ├── model 92 | ├── sae 93 | ├── transformers_model 94 | ├── utils 95 | train.py 96 | test.py 97 | ``` 98 | 99 | - The `benchmark` folder stores information related to benchmarks, such as the JSON files for ArenaHard questions. 100 | - The `config` folder contains YAML files needed to manage training parameters. 101 | - `data` handles the processing and loading of training data. 102 | - `feature_alignment` is the main directory containing the code for training and testing. 103 | - The `sae` subdirectory includes files related to sparse autoencoder models. 104 | - The `model` folder contains the Lightning Module framework for training. 105 | - `utils` includes other general utility functions. 106 | - The `transformers_model` directory has Hugging Face-structured model files (e.g., `modeling_xx`) to support custom models. 107 | - `outputs` is used to store generated outputs. 108 | - `train.py` and `test.py` are the main entry points for training and testing. 109 | 110 | --- 111 | 112 | ### 3. Creating a Custom Dataset (if needed) 113 | 114 | Want to load your own dataset? Add a function to dataloader.py like this: 115 | 116 | ```python 117 | def get_custom_dataset(split: str, ...): 118 | # Your dataset loading logic here 119 | return Dataset 120 | ``` 121 | 122 | Then, add your dataset to the yaml config: 123 | 124 | ```yaml 125 | datasets: 126 | - ultrabin 127 | - # [your custom dataset] 128 | ``` 129 | We support multiple datasets like SHP, HH, and Ultrachat. You can check the available datasets in the `data/dataloader.py`. 130 | 131 | --- 132 | 133 | ### 4. Creating a Custom Model (if needed) 134 | 135 | It's time to customize your method. If you want to support a new alignment method, you can try creating your own Lightning Module for training in `feature_alignment/model/your_custom_model.py`: 136 | 137 | ```python 138 | class CustomOModel(DPOModel): 139 | def a_method(self, ...): 140 | # Your method logic here 141 | return loss 142 | def get_batch_metrics(self, ...): 143 | # Your metrics logic here 144 | return loss, metrics 145 | ``` 146 | Please note that this is actually not "creating a model" but rather "creating a method". We recommend using the existing models as a template and replacing the method logic with your own. 147 | 148 | --- 149 | 150 | ### 5. 🚀 Training Your Model 151 | 152 | Train your model on datasets like SHP, HH, or OpenAssistant with one simple command: 153 | 154 | ```bash 155 | python train.py loss=sft model=llama7b 156 | ``` 157 | 158 | Override the default parameters by specifying them in the command line. 159 | 160 | --- 161 | 162 | ### 5. 🧪 Sampling and Evaluation 163 | 164 | After training, generate some samples with your new model using: 165 | 166 | ```bash 167 | python eval.py --config-path=config.yaml ++n_samples=512 ++model.eval_batch_size=32 ++samples_dir=samples/ 168 | ``` 169 | 170 | And evaluate those samples with **GPT-4** using: 171 | 172 | ```bash 173 | python compare.py -f samples/my_experiment.json -mc 512 -bk chosen -ck policy -r results.jsonl 174 | ``` 175 | 176 | --- 177 | 178 | ## 📚 Citation 179 | 180 | This project is built on top of [HALOs](https://github.com/ContextualAI/HALOs) and [Hydra-lightning](https://github.com/ashleve/lightning-hydra-template). 181 | 182 | If you find this repo or our paper useful, please feel free to cite us: 183 | 184 | ```bibtex 185 | @article{yin2024direct, 186 | title={Direct Preference Optimization Using Sparse Feature-Level Constraints}, 187 | author={Yin, Qingyu and Leong, Chak Tou and Zhang, Hongbo and Zhu, Minjun and Yan, Hanqi and Zhang, Qiang and He, Yulan and Li, Wenjie and Wang, Jun and Zhang, Yue and Yang, Linyi}, 188 | journal={arXiv preprint arXiv:2411.07618}, 189 | year={2024} 190 | } 191 | ``` 192 | 193 | --- 194 | 195 | -------------------------------------------------------------------------------- /assets/89F5EE60-13D9-416B-B395-8774B4350509.webp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/assets/89F5EE60-13D9-416B-B395-8774B4350509.webp -------------------------------------------------------------------------------- /assets/qrcode_1731259533808.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/assets/qrcode_1731259533808.jpg -------------------------------------------------------------------------------- /benchmark/test.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/benchmark/test.py -------------------------------------------------------------------------------- /config/config.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - _self_ 3 | - model: gemma-2-2b 4 | - loss: fpo 5 | 6 | debug: true 7 | seed: 39 # random seed 8 | exp_name: 3df-s1-1015 # name for this experiment in the local run directory and on wandb 9 | mode: predict # mode: one of 'train', 'eval', or 'sample' 10 | cache_dir: cache 11 | ckpt_dir: outputs/ckpt 12 | resume_ckpt: null # the path to a checkpoint to resume training from 13 | datasets: 14 | - ultrabin 15 | hf_token: null 16 | eval_bs: 1 # 50GB/80GB 17 | train_bs: 1 # micro-batch size i.e. on one GPU 18 | shuffle: true # if need to shuffle the data 19 | num_workers: 8 # number of workers for data loading 20 | n_epochs: null 21 | n_examples: 1000000 22 | n_eval_examples: 1000 23 | 24 | sae: 25 | sae_name_or_path: google/gemma-scope-2b-pt-res 26 | sae_layer_id: 25 27 | filename: "layer_25/width_16k/average_l0_55/params.npz" # if is a released model 28 | encoder: true 29 | decoder: false 30 | 31 | logger: 32 | neptune_project: null # null is None, TODO, need to change accordingly 33 | neptune_api_token: null # TODO 34 | wandb: 35 | enabled: true # wandb configuration 36 | entity: null 37 | project: "3D-Full-Attention" 38 | 39 | # callbacks settings 40 | callbacks: 41 | # - module_name: model_dit.utils.callbacks 42 | # class_name: BasicCallback 43 | # config: config 44 | - module_name: lightning.pytorch.callbacks 45 | class_name: ModelCheckpoint 46 | dirpath: ${ckpt_dir}/${exp_name} # where to save the checkpoints 47 | every_n_train_steps: 50 # how often to save checkpoints 48 | # filename: run_name + '{epoch}-{step}' # the filename for the checkpoints 49 | save_top_k: -1 # -1, save all checkpoints 50 | 51 | trainer: 52 | accelerator: gpu 53 | strategy: ddp 54 | # fsdp_sharding_strategy: "SHARD_GRAD_OP" 55 | # fsdp_state_dict_type: "full" 56 | devices: 2 57 | precision: bf16-mixed 58 | enable_checkpointing: true 59 | accumulate_grad_batches: 1 60 | gradient_clip_val: 1.0 61 | log_every_n_steps: 1 62 | val_check_interval: 32 # evaluate the model every eval_every steps 63 | 64 | # optimizer settings 65 | optimizer: 66 | lr: 5e-7 # the learning rate 67 | warmup_steps: 150 # number of linear warmup steps for the learning rate 68 | adam_beta1: 0.9 # beta1 for the Adam optimizer 69 | adam_beta2: 0.95 # beta2 for the Adam optimizer 70 | adam_epsilon: 1.0e-08 # epsilon for the Adam optimizer 71 | enable_xformers_memory_efficient_attention: true # whether to use the memory-efficient implementation of the attention layer 72 | gradient_accumulation_steps: 1 # number of steps to accumulate gradients over 73 | gradient_checkpointing: false # whether to use gradient checkpointing 74 | lr_scheduler: constant # the learning rate scheduler 75 | lr_warmup_steps: 1 # the number of warmup steps for the learning rate 76 | max_grad_norm: 1.0 # the maximum gradient norm 77 | max_train_steps: 300000 # the maximum number of training steps 78 | max_epochs: 1e9 # set a maximum number of epochs to train for 79 | mixed_precision: bf16 # the mixed precision mode 80 | scale_lr: false # whether to scale the learning rate 81 | weight_decay: 0.0001 # the weight decay 82 | use_8bit_adam: false # whether to use 8-bit Adam 83 | 84 | data: 85 | human_prefix: "\n<|user|>\n" 86 | assistant_prefix: "\n<|assistant|>\n" 87 | human_suffix: "" 88 | assistant_suffix: "" 89 | frac_unique_desirable: 1.0 90 | frac_unique_undesirable: 1.0 # for imbalance study 91 | 92 | inference: 93 | output_dir: outputs/inference 94 | num_inference_steps: 20 95 | classifier_free_guidance: false 96 | 97 | 98 | -------------------------------------------------------------------------------- /config/loss/cdpo.yaml: -------------------------------------------------------------------------------- 1 | # conservative Direct Preference Optimization 2 | name: cdpo 3 | 4 | # the temperature parameter for cDPO; lower values mean we care less about the reference model 5 | beta: 0.1 6 | 7 | # proportion of preferences with the wrong label 8 | epsilon: 0.2 9 | 10 | trainer: CDPOTrainer 11 | 12 | dataloader: PairedPreferenceDataLoader 13 | 14 | use_reference_model: true -------------------------------------------------------------------------------- /config/loss/csft.yaml: -------------------------------------------------------------------------------- 1 | # token-conditioned supervised finetuning, in the style of Korbak et al.'s (2023) "Pretraining Models with Human Feedback." 2 | # i.e., add a or token prior to the output during training, then postpend to the input for inference 3 | name: csft 4 | 5 | trainer: SFTTrainer 6 | 7 | dataloader: ConditionalSFTDataLoader 8 | 9 | use_reference_model: false 10 | 11 | chosen_control_token: "<|good|>" 12 | 13 | rejected_control_token: "<|bad|>" -------------------------------------------------------------------------------- /config/loss/dpo-sigmoid.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | # DO NOT USE dpo-sigmoid in practice: this is just for understanding the importance of convexity in the loss regime 3 | name: dpo-logsigmoid 4 | 5 | # the temperature parameter for DPO; lower values mean we care less about the reference model 6 | beta: 0.1 7 | 8 | trainer: DPOTrainer 9 | 10 | dataloader: PairedPreferenceDataLoader 11 | 12 | use_reference_model: true 13 | -------------------------------------------------------------------------------- /config/loss/dpo.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | name: dpo 3 | use_reference_model: true 4 | 5 | # the temperature parameter for DPO; lower values mean we care less about the reference model 6 | beta: 0.1 7 | 8 | dataloader: 9 | module_name: data.dataloader 10 | class_name: PairedPreferenceDataLoader 11 | 12 | model: 13 | module_name: feature_alignment.model.dpo 14 | class_name: DPOModel -------------------------------------------------------------------------------- /config/loss/fdpo-kl.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | name: fdpo-kl 3 | 4 | # beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. 5 | # alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence. 6 | beta: 0.2 7 | alpha: 0.7 8 | gamma: 0.0 9 | 10 | trainer: FDPOKLTrainer 11 | 12 | dataloader: PairedPreferenceDataLoader 13 | 14 | use_reference_model: true 15 | -------------------------------------------------------------------------------- /config/loss/fpo.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | name: fpo 3 | 4 | # beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. 5 | # alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence. 6 | beta: 0.1 7 | alpha: 0.5 8 | gamma: 0.1 9 | simpo: False 10 | use_mse: True 11 | use_reference_model: true 12 | 13 | dataloader: 14 | module_name: data.dataloader 15 | class_name: PairedPreferenceDataLoader 16 | 17 | model: 18 | module_name: feature_alignment.model.fpo 19 | class_name: FPOModel 20 | 21 | 22 | -------------------------------------------------------------------------------- /config/loss/kto-logsigmoid.yaml: -------------------------------------------------------------------------------- 1 | # Kahneman-Tversky Optimization with a log sigmoid term on the outside 2 | # DO NOT USE kto-logsigmoid in practice: this is just for understanding the importance of convexity in the loss regime 3 | name: kto-logsigmoid 4 | 5 | # the temperature parameter for KTO; lower values mean we care less about the reference model 6 | beta: 0.1 7 | 8 | trainer: KTOLogSigmoidTrainer 9 | 10 | dataloader: UnpairedPreferenceDataLoader 11 | 12 | use_reference_model: true 13 | 14 | # how much to weigh the losses of desirable examples (when dataset is imbalanced) 15 | desirable_weight: 1.0 16 | 17 | # how much to weigh the losses of undesirable examples (when dataset is imbalanced) 18 | undesirable_weight: 1.0 19 | -------------------------------------------------------------------------------- /config/loss/kto-simple.yaml: -------------------------------------------------------------------------------- 1 | # Kahneman-Tversky Optimization (legacy version) 2 | # assumes that data is 50% desirable and 50% undesirable examples 3 | name: kto-simple 4 | 5 | # the temperature parameter for KTO; lower values mean we care less about the reference model 6 | beta: 0.1 7 | 8 | trainer: SimpleKTOTrainer 9 | 10 | dataloader: SimpleKTODataLoader 11 | 12 | use_reference_model: true -------------------------------------------------------------------------------- /config/loss/kto-surprisal.yaml: -------------------------------------------------------------------------------- 1 | # Kahneman-Tversky Optimization using the token-level surprisal as the reward 2 | name: kto-surprisal 3 | 4 | # the temperature parameter for KTO; lower values mean we care less about the reference model 5 | beta: 0.1 6 | 7 | trainer: KTOSurprisalTrainer 8 | 9 | dataloader: UnpairedPreferenceDataLoader 10 | 11 | use_reference_model: false 12 | 13 | # how much to weigh the losses of desirable examples (when dataset is imbalanced) 14 | desirable_weight: 1.0 15 | 16 | # how much to weigh the losses of undesirable examples (when dataset is imbalanced) 17 | undesirable_weight: 1.0 -------------------------------------------------------------------------------- /config/loss/kto-zero.yaml: -------------------------------------------------------------------------------- 1 | # Kahneman-Tversky Optimization with a zero reward reference point (de facto similar to unlikelihood training by Welleck et al. (2019)) 2 | # DO NOT USE kto-zero in practice: this is just for understanding the importance of the KL term 3 | name: kto-zero 4 | 5 | # the temperature parameter for KTO; lower values mean we care less about the reference model 6 | beta: 0.1 7 | 8 | trainer: KTOZeroTrainer 9 | 10 | dataloader: UnpairedPreferenceDataLoader 11 | 12 | use_reference_model: true -------------------------------------------------------------------------------- /config/loss/kto.yaml: -------------------------------------------------------------------------------- 1 | # Kahneman-Tversky Optimization 2 | name: kto 3 | 4 | # the temperature parameter for KTO; lower values mean we care less about the reference model 5 | beta: 0.1 6 | 7 | trainer: KTOTrainer 8 | 9 | dataloader: UnpairedPreferenceDataLoader 10 | 11 | use_reference_model: true 12 | 13 | # how much to weigh the losses of desirable examples (when dataset is imbalanced) 14 | desirable_weight: 1.0 15 | 16 | # how much to weigh the losses of undesirable examples (when dataset is imbalanced) 17 | undesirable_weight: 1.0 -------------------------------------------------------------------------------- /config/loss/orpo.yaml: -------------------------------------------------------------------------------- 1 | # odds ratio preference optimization (ORPO) 2 | name: orpo 3 | 4 | # the temperature parameter for DPO; lower values mean we care less about the reference model 5 | OR_scale: 0.25 6 | 7 | trainer: ORPOTrainer 8 | 9 | dataloader: PairedPreferenceDataLoader 10 | 11 | use_reference_model: true -------------------------------------------------------------------------------- /config/loss/ppo.yaml: -------------------------------------------------------------------------------- 1 | # Proximal Policy Optimization 2 | name: ppo 3 | 4 | # number of times to iterate over the same PPO batch 5 | ppo_epochs: 1 6 | 7 | # used to clip the probability ratio in range [cliprange, 1/cliprange] 8 | cliprange: 0.5 9 | 10 | trainer: PPOTrainer 11 | 12 | dataloader: UnpairedPreferenceDataLoader 13 | 14 | # lambda for PPO 15 | lam: 0.95 16 | 17 | # gamma for PPO 18 | gamma: 0.99 19 | 20 | # coefficient on critic loss in PPO; adjusted magnitude of loss should be similar to policy loss 21 | critic_coef: 0.01 22 | 23 | # coefficient on KL penalty 24 | KL_coef: 0.1 25 | 26 | use_reference_model: true -------------------------------------------------------------------------------- /config/loss/sft.yaml: -------------------------------------------------------------------------------- 1 | # Supervised Finetuning 2 | name: sft 3 | use_reference_model: false 4 | 5 | dataloader: 6 | module_name: data.dataloader 7 | class_name: SFTBasicDataLoader 8 | 9 | model: 10 | module_name: feature_alignment.model.sft 11 | class_name: SFTModel -------------------------------------------------------------------------------- /config/loss/simpo.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | name: simpo 3 | use_reference_model: true 4 | # beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. 5 | # alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence. 6 | beta: 2.0 7 | alpha: 0.0 8 | gamma: 0.5 9 | 10 | 11 | 12 | dataloader: 13 | module_name: data.dataloader 14 | class_name: PairedPreferenceDataLoader 15 | 16 | model: 17 | module_name: feature_alignment.model.simpo 18 | class_name: SimPOModel -------------------------------------------------------------------------------- /config/loss/slic.yaml: -------------------------------------------------------------------------------- 1 | # Sequence Likelihood Calibration: https://arxiv.org/abs/2305.10425 2 | name: slic 3 | 4 | # the temperature parameter for SLiC 5 | beta: 1.0 6 | 7 | # lambda value for KL penalty 8 | lambda_coef: 0.1 9 | 10 | trainer: SLiCTrainer 11 | 12 | dataloader: PairedPreferenceDataLoader 13 | 14 | use_reference_model: false -------------------------------------------------------------------------------- /config/loss/tdpo1.yaml: -------------------------------------------------------------------------------- 1 | # Direct Preference Optimization 2 | name: tdpo1 3 | use_reference_model: true 4 | # beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. 5 | # alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence. 6 | beta: 0.1 7 | alpha: 0.5 8 | 9 | dataloader: 10 | module_name: data.dataloader 11 | class_name: PairedPreferenceDataLoader 12 | 13 | model: 14 | module_name: feature_alignment.model.tdpo 15 | class_name: TDPO1Model -------------------------------------------------------------------------------- /config/loss/tdpo2.yaml: -------------------------------------------------------------------------------- 1 | name: tdpo2 2 | use_reference_model: true 3 | # beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. 4 | # alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence. 5 | beta: 0.1 6 | alpha: 0.5 7 | 8 | dataloader: 9 | module_name: data.dataloader 10 | class_name: PairedPreferenceDataLoader 11 | 12 | model: 13 | module_name: feature_alignment.model.tdpo 14 | class_name: TDPO2Model -------------------------------------------------------------------------------- /config/model/base_model.yaml: -------------------------------------------------------------------------------- 1 | # the name of the model to use; should be something like 2 | # gpt2-xl or gpt-neo-2.7B or huggyllama/llama-7b 3 | name_or_path: ??? 4 | 5 | # the name of the tokenizer to use; if null, will use the tokenizer from the model 6 | tokenizer_name_or_path: null 7 | 8 | # override pre-trained weights (e.g., from SFT); optional, should be the name of the model (e.g., archangel_sft_pythia-1.4b/LATEST/policy.pt) 9 | load_from: null 10 | 11 | # the name of the module class to wrap with FSDP; should be something like 12 | # e.g. GPT2Block, GPTNeoXLayer, LlamaDecoderLayer, etc. 13 | block_name: null 14 | 15 | # the dtype for the policy parameters/optimizer state 16 | policy_dtype: bfloat16 17 | 18 | # the mixed precision dtype if using FSDP; defaults to the same as the policy 19 | fsdp_policy_mp: null 20 | 21 | # the dtype for the reference model (which is used for inference only) 22 | reference_dtype: bfloat16 23 | 24 | # the maximum gradient norm to clip to 25 | max_grad_norm: 10.0 26 | 27 | # gradient norm for clipping gradient of value head (for PPO) 28 | v_head_max_grad_norm: 0.10 29 | 30 | # the maximum allowed length for an input (prompt + response) (usually has to be smaller than what the model supports) 31 | max_length: 2048 32 | 33 | # the maximum allowed length for a prompt (remainder will be dedicated to the completion) 34 | max_prompt_length: 1024 35 | 36 | activation_checkpointing: true 37 | 38 | # the total batch size for training; for FSDP, divide by number of devices to get microbatch size 39 | batch_size: 32 40 | 41 | # number of steps to accumulate over for each batch 42 | gradient_accumulation_steps: 1 43 | 44 | # the batch size during evaluation and sampling, if enabled 45 | eval_batch_size: 16 46 | 47 | use_flash_attention: false -------------------------------------------------------------------------------- /config/model/gemma-2-2b.yaml: -------------------------------------------------------------------------------- 1 | module_name: transformers 2 | class_name: AutoModelForCausalLM 3 | hf_model_name_or_path: google/gemma-2-2b 4 | hf_tokenizer_name_or_path: google/gemma-2-2b 5 | max_length: 1024 6 | max_prompt_length: 1024 7 | use_flash_attention: true 8 | sae_layer_id: 25 9 | 10 | -------------------------------------------------------------------------------- /config/model/gemma-2-9b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: google/gemma-2-9b 5 | block_name: Gemma2DecoderLayer 6 | use_flash_attention: true 7 | sae_layer_id: 25 -------------------------------------------------------------------------------- /config/model/llama13b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: huggyllama/llama-13b 5 | block_name: LlamaDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/llama30b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: huggyllama/llama-30b 5 | block_name: LlamaDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/llama65b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: huggyllama/llama-65b 5 | block_name: LlamaDecoderLayer 6 | use_flash_attention: true 7 | 8 | batch_size: 16 9 | gradient_accumulation_steps: 4 -------------------------------------------------------------------------------- /config/model/llama7b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: huggyllama/llama-7b 5 | block_name: LlamaDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/mistral7b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: mistralai/Mistral-7B-v0.1 5 | block_name: MistralDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/mistral7b_instruct.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: mistralai/Mistral-7B-Instruct-v0.1 5 | block_name: MistralDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/mistral7b_sft_beta.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: HuggingFaceH4/mistral-7b-sft-beta 5 | block_name: MistralDecoderLayer 6 | use_flash_attention: true 7 | -------------------------------------------------------------------------------- /config/model/pythia1-4b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: EleutherAI/pythia-1.4b 5 | block_name: GPTNeoXLayer -------------------------------------------------------------------------------- /config/model/pythia12-0b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: EleutherAI/pythia-12b 5 | block_name: GPTNeoXLayer -------------------------------------------------------------------------------- /config/model/pythia2-8b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: EleutherAI/pythia-2.8b 5 | block_name: GPTNeoXLayer -------------------------------------------------------------------------------- /config/model/pythia6-9b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: EleutherAI/pythia-6.9b 5 | block_name: GPTNeoXLayer -------------------------------------------------------------------------------- /config/model/qwen-2-1.5b.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: /data/models/Qwen2-1.5B 5 | block_name: Qwen2DecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /config/model/zephyr-sft-beta.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - base_model 3 | 4 | name_or_path: HuggingFaceH4/mistral-7b-sft-beta 5 | block_name: MistralDecoderLayer 6 | use_flash_attention: true -------------------------------------------------------------------------------- /debug.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoModel 2 | from huggingface_hub import login 3 | 4 | login(token="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") 5 | model = AutoModel.from_pretrained("google/gemma-2-2b") -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: halos_v2 2 | channels: 3 | - pytorch 4 | - nvidia 5 | - conda-forge 6 | - defaults 7 | dependencies: 8 | - _libgcc_mutex=0.1=conda_forge 9 | - _openmp_mutex=4.5=2_kmp_llvm 10 | - blas=2.116=mkl 11 | - blas-devel=3.9.0=16_linux64_mkl 12 | - brotli-python=1.1.0=py312h30efb56_1 13 | - bzip2=1.0.8=hd590300_5 14 | - ca-certificates=2024.2.2=hbcca054_0 15 | - certifi=2024.2.2=pyhd8ed1ab_0 16 | - charset-normalizer=3.3.2=pyhd8ed1ab_0 17 | - cuda-cudart=12.1.105=0 18 | - cuda-cupti=12.1.105=0 19 | - cuda-libraries=12.1.0=0 20 | - cuda-nvrtc=12.1.105=0 21 | - cuda-nvtx=12.1.105=0 22 | - cuda-opencl=12.3.101=0 23 | - cuda-runtime=12.1.0=0 24 | - ffmpeg=4.3=hf484d3e_0 25 | - filelock=3.13.1=pyhd8ed1ab_0 26 | - freetype=2.12.1=h267a509_2 27 | - gmp=6.3.0=h59595ed_0 28 | - gnutls=3.6.13=h85f3911_1 29 | - icu=73.2=h59595ed_0 30 | - idna=3.6=pyhd8ed1ab_0 31 | - jinja2=3.1.3=pyhd8ed1ab_0 32 | - jpeg=9e=h166bdaf_2 33 | - lame=3.100=h166bdaf_1003 34 | - lcms2=2.15=hfd0df8a_0 35 | - ld_impl_linux-64=2.40=h41732ed_0 36 | - lerc=4.0.0=h27087fc_0 37 | - libblas=3.9.0=16_linux64_mkl 38 | - libcblas=3.9.0=16_linux64_mkl 39 | - libcublas=12.1.0.26=0 40 | - libcufft=11.0.2.4=0 41 | - libcufile=1.8.1.2=0 42 | - libcurand=10.3.4.107=0 43 | - libcusolver=11.4.4.55=0 44 | - libcusparse=12.0.2.55=0 45 | - libdeflate=1.17=h0b41bf4_0 46 | - libexpat=2.5.0=hcb278e6_1 47 | - libffi=3.4.2=h7f98852_5 48 | - libgcc-ng=13.2.0=h807b86a_5 49 | - libgfortran-ng=13.2.0=h69a702a_5 50 | - libgfortran5=13.2.0=ha4646dd_5 51 | - libhwloc=2.9.3=default_h554bfaf_1009 52 | - libiconv=1.17=hd590300_2 53 | - libjpeg-turbo=2.0.0=h9bf148f_0 54 | - liblapack=3.9.0=16_linux64_mkl 55 | - liblapacke=3.9.0=16_linux64_mkl 56 | - libnpp=12.0.2.50=0 57 | - libnsl=2.0.1=hd590300_0 58 | - libnvjitlink=12.1.105=0 59 | - libnvjpeg=12.1.1.14=0 60 | - libpng=1.6.42=h2797004_0 61 | - libsqlite=3.45.1=h2797004_0 62 | - libstdcxx-ng=13.2.0=h7e041cc_5 63 | - libtiff=4.5.0=h6adf6a1_2 64 | - libuuid=2.38.1=h0b41bf4_0 65 | - libwebp-base=1.3.2=hd590300_0 66 | - libxcrypt=4.4.36=hd590300_1 67 | - libxml2=2.12.5=h232c23b_0 68 | - libzlib=1.2.13=hd590300_5 69 | - llvm-openmp=15.0.7=h0cdce71_0 70 | - markupsafe=2.1.5=py312h98912ed_0 71 | - mkl=2022.1.0=h84fe81f_915 72 | - mkl-devel=2022.1.0=ha770c72_916 73 | - mkl-include=2022.1.0=h84fe81f_915 74 | - mpmath=1.3.0=pyhd8ed1ab_0 75 | - ncurses=6.4=h59595ed_2 76 | - nettle=3.6=he412f7d_0 77 | - networkx=3.2.1=pyhd8ed1ab_0 78 | - numpy=1.26.4=py312heda63a1_0 79 | - openh264=2.1.1=h780b84a_0 80 | - openjpeg=2.5.0=hfec8fc6_2 81 | - openssl=3.2.1=hd590300_0 82 | - pillow=10.2.0=py312h5eee18b_0 83 | - pip=24.0=pyhd8ed1ab_0 84 | - pysocks=1.7.1=pyha2e5f31_6 85 | - python=3.12.1=hab00c5b_1_cpython 86 | - python_abi=3.12=4_cp312 87 | - pytorch=2.2.0=py3.12_cuda12.1_cudnn8.9.2_0 88 | - pytorch-cuda=12.1=ha16c6d3_5 89 | - pytorch-mutex=1.0=cuda 90 | - pyyaml=6.0.1=py312h98912ed_1 91 | - readline=8.2=h8228510_1 92 | - requests=2.31.0=pyhd8ed1ab_0 93 | - setuptools=69.0.3=pyhd8ed1ab_0 94 | - sympy=1.12=pyh04b8f61_3 95 | - tbb=2021.11.0=h00ab1b0_1 96 | - tk=8.6.13=noxft_h4845f30_101 97 | - torchaudio=2.2.0=py312_cu121 98 | - torchvision=0.17.0=py312_cu121 99 | - typing_extensions=4.9.0=pyha770c72_0 100 | - urllib3=2.2.0=pyhd8ed1ab_0 101 | - wheel=0.42.0=pyhd8ed1ab_0 102 | - xz=5.2.6=h166bdaf_0 103 | - yaml=0.2.5=h7f98852_2 104 | - zlib=1.2.13=hd590300_5 105 | - zstd=1.5.5=hfc55251_0 106 | - pip: 107 | - accelerate==0.21.0 108 | - aiohttp==3.9.3 109 | - aiosignal==1.3.1 110 | - annotated-types==0.6.0 111 | - antlr4-python3-runtime==4.9.3 112 | - anyio==4.2.0 113 | - appdirs==1.4.4 114 | - attrs==23.2.0 115 | - click==8.1.7 116 | - datasets==2.17.0 117 | - dill==0.3.6 118 | - distro==1.9.0 119 | - docker-pycreds==0.4.0 120 | - einops==0.7.0 121 | - flash-attn==2.3.3 122 | - frozenlist==1.4.1 123 | - fsspec==2023.10.0 124 | - gitdb==4.0.11 125 | - gitpython==3.1.41 126 | - h11==0.14.0 127 | - httpcore==1.0.2 128 | - httpx==0.26.0 129 | - huggingface-hub==0.20.3 130 | - hydra-core==1.3.2 131 | - multidict==6.0.5 132 | - multiprocess==0.70.14 133 | - ninja==1.11.1.1 134 | - omegaconf==2.3.0 135 | - openai==1.12.0 136 | - packaging==23.2 137 | - pandas==2.2.0 138 | - protobuf==4.25.2 139 | - psutil==5.9.8 140 | - pyarrow==15.0.0 141 | - pyarrow-hotfix==0.6 142 | - pydantic==2.6.1 143 | - pydantic-core==2.16.2 144 | - python-dateutil==2.8.2 145 | - pytz==2024.1 146 | - regex==2023.12.25 147 | - responses==0.18.0 148 | - safetensors==0.4.2 149 | - sentry-sdk==1.40.3 150 | - setproctitle==1.3.3 151 | - six==1.16.0 152 | - smmap==5.0.1 153 | - sniffio==1.3.0 154 | - tokenizers==0.15.2 155 | - tqdm==4.66.2 156 | - transformers==4.35.2 157 | - tzdata==2024.1 158 | - wandb==0.16.3 159 | - xxhash==3.4.1 160 | - yarl==1.9.4 161 | variables: 162 | TRANSFORMERS_CACHE: /data/huggingface/hub 163 | HF_HOME: /data/huggingface/ 164 | HF_DATASETS_CACHE: /data/huggingface/datasets 165 | prefix: /opt/conda/envs/halos3 166 | -------------------------------------------------------------------------------- /feature_alignment/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/feature_alignment/__init__.py -------------------------------------------------------------------------------- /feature_alignment/compare.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2023 Contextual AI, Inc. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Compare a candidate model to some baseline model by using GPT4 as an judge. 8 | Typical use is 9 | 10 | python compare.py -f samples/sft_llama7b.json -mc 512 -bk chosen -ck policy -r result.jsonl -j gpt-4-0613 11 | 12 | where 13 | -f is a JSON file of generations, where the "samples" key maps to a list of dicts of the form 14 | { 15 | history_key: the prompt, 16 | baseline_key: the generation by the baseline (this can be model-written (Anthropic-HH) or human-written (SHP)), 17 | candidate_key: the generation by the candidate model you want to evaluate, 18 | } 19 | - mc denotes the maximum number of comparisons to make between baseline_key and candidate_key (optional) 20 | - bk is the baseline model's key in the dict (optional, default: chosen) 21 | - ck is the candidate model's key in the dict (optional, default: policy) 22 | - r is the JSONL file to which to append the result, a JSON dict containing the metadata, the number of winning matchups by each model, and the lengths of all outputs 23 | - j is the version of GPT to use as a judge (optional, default: gpt-4-0613) 24 | 25 | To overwrite the template used to evaluate with GPT-4 as a judge, subclass PromptTemplate. 26 | The default template asks GPT-4 to pick the response that is "more helpful, harmless, and concise", since helpfulness and harmlessness are the two key objectives of model alignment and GPT-4 has a bias for longer outputs by default. 27 | If GPT-4's response does not contain 'Response 1' or 'Response 2' (case-insensitive), then we assume that no winner is picked and it does not count as a win for either model. 28 | Therefore the number of baseline wins and the number of candidate wins add up to less total # of comparisons. 29 | """ 30 | 31 | import os 32 | import openai 33 | import random 34 | import json 35 | import numpy as np 36 | import re 37 | import time 38 | import signal 39 | import pandas as pd 40 | from dataclasses import dataclass 41 | from scipy.stats import binomtest, binom 42 | from math import ceil, floor 43 | from typing import Dict, Tuple 44 | from collections import defaultdict 45 | from datetime import datetime 46 | from transformers import AutoTokenizer 47 | 48 | client = openai.OpenAI( 49 | api_key=os.environ.get("OPENAI_API_KEY"), 50 | ) 51 | 52 | import argparse 53 | parser = argparse.ArgumentParser() 54 | parser.add_argument('--file', '-f', help="JSON file with the generated samples; list of dicts containing candidate, baseline, and history as keys", type= str) 55 | parser.add_argument('--candidate_key', '-ck', help="model that you want to test; should be a key in the JSON dicts", type=str, default='policy') 56 | parser.add_argument('--baseline_key', '-bk', help="model that you want to use as a baseline; should be a key in the JSON dicts", type=str, default='chosen') 57 | parser.add_argument('--history_key', '-hk', help="key for prompt; should be a key in the JSON dicts", type=str, default='prompt') 58 | parser.add_argument('--labels', '-l', help="used to enumerate the responses being compared in the GPT-4 API call (e.g., Response 1, Response A)", type=str, default='12') 59 | parser.add_argument('--seed', '-s', help="seed for GPT eval", type=int, default=0) 60 | parser.add_argument('--sleep_time', '-st', help="how long to sleep to prevent rate limit hit", type=int, default=0.5) 61 | parser.add_argument('--max_comp', '-mc', help="maximum number of comparisons to make", type=int, default=None) 62 | parser.add_argument('--verbose', '-v', help="detailed outputs", type=bool, default=True) 63 | parser.add_argument('--results_file', '-r', help="JSONL file to append to", type=str, default='results.jsonl') 64 | parser.add_argument('--judge', '-j', help="version of GPT-4 used as judge", type=str, default='gpt-4-0613') 65 | parser.add_argument('--save_csv', '-csv', help="where to save a CSV of individual judgments (don't save if empty string)", type=str, default='') 66 | 67 | 68 | class APITimeoutException(Exception): 69 | pass 70 | 71 | 72 | @dataclass 73 | class PromptTemplate: 74 | """ 75 | Prompt generator for comparing the outputs of any number of models using GPT-4 as a judge. 76 | """ 77 | models: Tuple[str] # list of models under consideration 78 | labels: str # list of labels to assign to models (e.g., "12345") 79 | seed: int # random seed 80 | verbose: bool 81 | human_prefix: str="\n<|user|>\n" 82 | assistant_prefix: str="\n<|assistant|>\n" # Tulu format; modify as needed 83 | 84 | def __post_init__(self): 85 | random.seed(self.seed) 86 | 87 | def shuffle(self): 88 | """ 89 | Shuffle the list of models under consideration. This is to correct for any bias GPT-4 might have 90 | in picking the first/last response. 91 | """ 92 | random.shuffle(self.models) 93 | 94 | def clean_response(self, response): 95 | """ 96 | Truncate any hanging sentence (not ending in a ?!.) and return the cleaned response. 97 | """ 98 | match = re.search(r'[.!?](?=[^.!?]*$)', response) 99 | if match: 100 | return response[:match.start() + 1] 101 | else: 102 | return response 103 | 104 | def format(self, history: str, samples: Dict[str, str]) -> str: 105 | """ 106 | Format samples into a given prompt. Overwrite in subclass as needed. 107 | 108 | Args: 109 | - history: the input/instruction 110 | - samples: a dictionary mapping models to their generations 111 | 112 | Returns: 113 | A list of messages to send to the GPT-4 API. Each item is a dictionary containing the role of the speaker 114 | and the candidate text. 115 | """ 116 | self.shuffle() 117 | history = history[:history.rfind(self.assistant_prefix)].strip() 118 | messages = [{"role": "user", "content": f"Here is a conversation between {self.human_prefix.strip()} and {self.assistant_prefix.strip()}.\n\n" + history }] 119 | prompt = [f'{self.assistant_prefix.strip()} can now choose from different responses.'] 120 | 121 | for i,m in enumerate(self.models): 122 | response = self.clean_response(samples[m]) 123 | prompt.append(f'Response {self.labels[i]}: {response}\nEND OF RESPONSE {self.labels[i]}') 124 | 125 | prompt.append("Between these two responses, which response is more helpful, harmless, and concise? Pick one.") 126 | 127 | messages.append({ 128 | "role": "user", 129 | "content": "\n\n".join(prompt), 130 | }) 131 | 132 | return messages 133 | 134 | def get_model_choice_from_response(self, response) -> str: 135 | """ 136 | Given a response from the GPT-4 evaluator, identify and return the model it chose. 137 | 138 | Args: 139 | - response: response from calling GPT-4 API 140 | 141 | Returns: 142 | One of the models in self.models (or None if LLM judge's choice cannot be inferred). 143 | """ 144 | completion = response.choices[0].message.content 145 | answer = re.search(r'response (.).*', completion, re.IGNORECASE) 146 | 147 | if self.verbose: 148 | print(completion) 149 | 150 | if answer is None: 151 | return None 152 | 153 | idx = self.labels.index(answer.group(1)) 154 | return self.models[idx] 155 | 156 | 157 | def get_preferred_model(history: str, samples: Dict[str, str], prompt_template: PromptTemplate, judge: str, rate_limit_size: int=1000) -> str: 158 | """ 159 | Find the model whose generation is most preferred by the judge. 160 | 161 | Args: 162 | - history: prompt used to condition generations 163 | - samples: generations for the given history, indexed by model name 164 | - prompt_template: instance of PromptTemplate 165 | - judge: one of the OpenAI chat models 166 | - rate_limit_size: maximum number of characters that can be in any message to avoid rate limit problem (tokens is ~ 1/3 of chars) 167 | 168 | Returns: 169 | The name of the more preferred model. 170 | """ 171 | # Set up a timeout handler 172 | def timeout_handler(signum, frame): 173 | """Handler for when OpenAI call takes too long.""" 174 | raise APITimeoutException("API call took too long") 175 | signal.signal(signal.SIGALRM, timeout_handler) 176 | signal.alarm(10) 177 | 178 | try: 179 | response = client.chat.completions.create( 180 | model=judge, 181 | messages=prompt_template.format(history, samples), 182 | temperature=0, 183 | max_tokens=10, 184 | seed=prompt_template.seed, 185 | ) 186 | 187 | signal.alarm(0) # Cancel the alarm since the call completed within the timeout 188 | return prompt_template.get_model_choice_from_response(response) 189 | except ValueError: 190 | print("The chosen response could not be determined.") 191 | pass 192 | except APITimeoutException: 193 | pass 194 | except openai.APIConnectionError as e: 195 | print("The server could not be reached.") 196 | print(e.__cause__) # an underlying Exception, likely raised within httpx. 197 | except openai.RateLimitError as e: 198 | print("A 429 status code was received; we should back off a bit.") 199 | signal.alarm(0) 200 | time.sleep(5) 201 | except openai.APIStatusError as e: 202 | print("Another non-200-range status code was received") 203 | print(e.response) 204 | finally: 205 | signal.alarm(0) 206 | 207 | return None 208 | 209 | 210 | if __name__ == "__main__": 211 | args = parser.parse_args() 212 | 213 | samples = json.load(open(args.file)) 214 | prompt_template = PromptTemplate( 215 | [args.candidate_key, args.baseline_key], 216 | args.labels, 217 | args.seed, 218 | verbose=args.verbose, 219 | human_prefix=samples['config']['human_prefix'], 220 | assistant_prefix=samples['config']['assistant_prefix'] 221 | ) 222 | tokenizer = AutoTokenizer.from_pretrained(samples['config']['local_run_dir']) 223 | 224 | i = 0 225 | lengths = defaultdict(list) 226 | wins = defaultdict(lambda: 0) 227 | individual_judgments = [] 228 | 229 | for batch in samples["samples"]: 230 | if args.max_comp is not None and i >= args.max_comp: 231 | break 232 | 233 | lengths[args.candidate_key].append(len(tokenizer.encode(batch[args.candidate_key]))) 234 | lengths[args.baseline_key].append(len(tokenizer.encode(batch[args.baseline_key]))) 235 | 236 | time.sleep(args.sleep_time) 237 | choice = get_preferred_model(batch[args.history_key], batch, prompt_template, judge=args.judge) 238 | i += 1 239 | 240 | if choice is not None: 241 | wins[choice] += 1 242 | 243 | if args.verbose: 244 | print(wins, 'of', i, { k: np.mean(lengths[k]) for k in lengths }) 245 | 246 | # save individual judgments 247 | if args.save_csv: 248 | if random.random() > 0.5: 249 | src_A, src_B = args.candidate_key, args.baseline_key 250 | response_A, response_B = batch[args.candidate_key], batch[args.baseline_key] 251 | else: 252 | src_B, src_A = args.candidate_key, args.baseline_key 253 | response_B, response_A = batch[args.candidate_key], batch[args.baseline_key] 254 | 255 | individual_judgments.append({ 256 | 'input' : batch[args.history_key].strip(), 257 | 'src_B' : src_B, 258 | 'src_A' : src_A, 259 | 'response_A': response_A.strip(), 260 | 'response_B': response_B.strip(), 261 | 'gpt4_choice' : ('A' if src_A == choice else 'B') 262 | }) 263 | 264 | results = { 265 | 'date': str(datetime.now()), 266 | 'total': i, 267 | 'seed': args.seed, 268 | 'exp_name': samples["config"]["exp_name"], 269 | 'judge' : args.judge, 270 | 'candidate': { 271 | 'name': args.candidate_key, 272 | 'wins': wins[args.candidate_key], 273 | 'lengths': lengths[args.candidate_key], 274 | }, 275 | 'baseline': { 276 | 'name': args.baseline_key, 277 | 'wins': wins[args.baseline_key], 278 | 'lengths': lengths[args.baseline_key], 279 | }, 280 | 'config' : samples["config"], 281 | } 282 | 283 | with open(args.results_file, 'a+') as f: 284 | json.dump(results, f) 285 | f.write('\n') 286 | 287 | if args.save_csv: 288 | pd.DataFrame.from_dict(individual_judgments).to_csv(args.save_csv) -------------------------------------------------------------------------------- /feature_alignment/eval.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2023 Contextual AI, Inc. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Main script for running evals. This will run an eval according to the specified config, which should be a YAML file generated during training. 8 | You must override the mode from 'train' to one of 'sample', 'eval', or 'alpacaeval'. 9 | Overriding the other config parameters is optional. 10 | 11 | For sampling, do something like: 12 | python eval.py --config-path=/data/models/archangel/archangel_sft_pythia1-4b ++mode=sample ++n_samples=512 ++model.eval_batch_size=32 13 | 14 | For calculating the batch metrics (e.g., accuracy of predicted preference direction when preference is inferred from DPO rewards) on a held-out set: 15 | python eval.py --config-path=/data/models/archangel/archangel_sft_pythia1-4b ++mode=eval 16 | 17 | To sample from the unaligned model (e.g., the original EleutherAI/pythia1-4b), add ++saved_policy=null to the command. 18 | 19 | To sample from every prompt (without limit), set ++n_samples=null 20 | """ 21 | import torch 22 | torch.backends.cuda.matmul.allow_tf32 = True 23 | import transformers 24 | from transformers import set_seed 25 | from utils import disable_dropout, init_distributed, get_open_port, rank0_print 26 | import os 27 | import hydra 28 | import torch.multiprocessing as mp 29 | from omegaconf import OmegaConf, DictConfig 30 | import json 31 | import socket 32 | from typing import Optional, Set 33 | from trainers import BasicTrainer, DPOTrainer 34 | import datasets.dataloader as dataloader 35 | from datetime import datetime 36 | 37 | 38 | @hydra.main(version_base=None, config_path="config", config_name="config") 39 | def main(config: DictConfig): 40 | """Main entry point for evaluating. Validates config, loads model(s), and kicks off worker process(es).""" 41 | # Resolve hydra references, e.g. so we don't re-compute the run directory 42 | OmegaConf.resolve(config) 43 | print(OmegaConf.to_yaml(config)) 44 | 45 | if config.mode not in ['sample', 'eval', 'alpacaeval', 'arenahard']: 46 | raise Exception("This is a script for eval/sampling. config.mode should be one of 'sample', 'eval', or 'alpacaeval'") 47 | 48 | set_seed(config.seed) 49 | 50 | print('=' * 80) 51 | print(f'Writing to', config.samples_dir) 52 | print('=' * 80) 53 | 54 | # purely inference, so put as much as possible onto the first gpu 55 | model_kwargs = {'device_map': "balanced_low_0"} 56 | 57 | tokenizer_name_or_path = config.local_run_dir or config.model.tokenizer_name_or_path # first see if saved tokenizer is in the experiment directory 58 | print(f'Loading tokenizer at {tokenizer_name_or_path}') 59 | tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_name_or_path) 60 | if tokenizer.pad_token_id is None: 61 | tokenizer.pad_token_id = tokenizer.eos_token_id 62 | 63 | print('building policy') 64 | policy_dtype = getattr(torch, config.model.policy_dtype) 65 | policy = transformers.AutoModelForCausalLM.from_pretrained( 66 | config.model.name_or_path, low_cpu_mem_usage=True, use_flash_attention_2=config.model.use_flash_attention, torch_dtype=policy_dtype, **model_kwargs) 67 | # note that models were only resized for csft before saving 68 | # important because number of tokens in pretrained tokenizer is different from model.config.vocab_size, 69 | # so resizing at eval will throw an error if not resized before training 70 | if config.loss.name == 'csft': 71 | policy.resize_token_embeddings(len(tokenizer)) # model being loaded should already be trained with additional tokens for this to be valid 72 | disable_dropout(policy) 73 | 74 | # saved policy can be force set to null to sample from pretrained model 75 | if config.saved_policy is not None: 76 | state_dict = torch.load(os.path.join(config.cache_dir, config.saved_policy), map_location='cpu') 77 | step, metrics = state_dict['step_idx'], state_dict['metrics'] 78 | print(f'loading pre-trained weights for policy at step {step} from {config.saved_policy} with metrics {json.dumps(metrics, indent=2)}') 79 | policy.load_state_dict(state_dict['state'], strict=False) 80 | 81 | if config.mode == 'eval' and config.loss.use_reference_model: 82 | print('building reference model') 83 | reference_model_dtype = getattr(torch, config.model.reference_dtype) 84 | reference_model = transformers.AutoModelForCausalLM.from_pretrained( 85 | config.model.name_or_path, low_cpu_mem_usage=True, use_flash_attention_2=config.model.use_flash_attention, torch_dtype=reference_model_dtype, **model_kwargs) 86 | 87 | if config.loss.name == 'ppo': 88 | reference_model.resize_token_embeddings(len(tokenizer)) 89 | 90 | disable_dropout(reference_model) 91 | 92 | if config.model.load_from is not None: 93 | state_dict = torch.load(os.path.join(config.cache_dir, config.model.load_from), map_location='cpu') 94 | step, metrics = state_dict['step_idx'], state_dict['metrics'] 95 | print(f'loading pre-trained weights for reference at step {step} from {config.model.load_from} with metrics {json.dumps(metrics, indent=2)}') 96 | reference_model.load_state_dict(state_dict['state']) 97 | else: 98 | reference_model = None 99 | 100 | data_loader_class = getattr(dataloader, config.loss.dataloader) 101 | data_iterator_kwargs = dict( 102 | max_length=config.model.max_length, 103 | max_prompt_length=config.model.max_prompt_length, 104 | # since the human/asst fields are not in the configs of the already-released models, add defaults 105 | human_prefix=config['human_prefix'], 106 | human_suffix=config['human_suffix'], 107 | assistant_prefix=config['assistant_prefix'], 108 | assistant_suffix=config['assistant_suffix'], 109 | seed=config.seed, 110 | # the following kwargs can be used to make dataset imbalanced (only used by UnbalancedUnpairedPreferenceDataLoader) 111 | frac_unique_desirable=config.get('frac_unique_desirable', 1.0), 112 | frac_unique_undesirable=config.get('frac_unique_undesirable', 1.0), 113 | # control tokens taken from Korbak et al.'s (2023) "Pretraining Models with Human Feedback" 114 | # SFTDataLoader will use them for sampling; ConditionalSFTDataLoader for training 115 | chosen_control_token=(config.loss.chosen_control_token if config.loss.name == "csft" else None), 116 | rejected_control_token=(config.loss.rejected_control_token if config.loss.name == "csft" else None), 117 | ) 118 | 119 | if config.mode == 'sample': 120 | print(f'Loading dataloader') 121 | os.makedirs(config.samples_dir, exist_ok=True) 122 | 123 | # use the SFT dataloader because we don't want to repeat prompts 124 | # and bc data ordering is different in paired vs unpaired data loaders 125 | # this way, sampled prompts are the same for a given seed 126 | eval_iterator = dataloader.SFTDataLoader( 127 | config.datasets, 128 | tokenizer, 129 | split='test', 130 | batch_size=config.model.eval_batch_size, 131 | n_examples=config.n_samples, 132 | max_prompt_count=1, 133 | **data_iterator_kwargs 134 | ) 135 | 136 | trainer = BasicTrainer(tokenizer, config, None, eval_iterator, policy, reference_model=reference_model) 137 | samples = trainer.sample() 138 | fn = os.path.join(config.samples_dir, f'{config.exp_name}.json') 139 | json.dump({ 140 | 'sampled_at' : str(datetime.now()), 141 | 'config' : OmegaConf.to_container(config, resolve=True), 142 | 'samples' : samples, 143 | }, open(fn, 'w'), indent=2) 144 | elif config.mode == 'eval': 145 | print(f'Loading dataloader') 146 | eval_iterator = data_loader_class( 147 | config.datasets, 148 | tokenizer, 149 | split='test', 150 | batch_size=config.model.eval_batch_size, 151 | n_examples=config.n_eval_examples, 152 | n_epochs=(1 if config.n_eval_examples is None else None), 153 | **data_iterator_kwargs 154 | ) 155 | 156 | trainer = DPOTrainer(tokenizer, config, None, eval_iterator, policy, reference_model=reference_model) 157 | results = trainer.eval() 158 | rank0_print(results) 159 | elif config.mode == 'alpacaeval': 160 | print(f'Loading dataloader') 161 | os.makedirs(config.samples_dir, exist_ok=True) 162 | 163 | eval_iterator = dataloader.SFTDataLoader( 164 | ['alpacaeval'], 165 | tokenizer, 166 | split='test', 167 | batch_size=config.model.eval_batch_size, 168 | n_examples=None, 169 | n_epochs=1, 170 | **data_iterator_kwargs 171 | ) 172 | 173 | trainer = BasicTrainer(tokenizer, config, None, eval_iterator, policy, reference_model=reference_model) 174 | samples = trainer.sample(include_original_prompt=True) 175 | alpaca_formatted_examples = [] 176 | 177 | for sample in samples: 178 | alpaca_formatted_examples.append({ 179 | 'instruction' : sample['original_prompt'], 180 | 'output': sample['policy'].strip(), 181 | 'reference' : sample['chosen'].strip(), 182 | }) 183 | 184 | fn = os.path.join(config.samples_dir, f'alpaca_{config.exp_name}.json') 185 | json.dump(alpaca_formatted_examples, open(fn, 'w'), indent=2) 186 | elif config.mode == 'arenahard': 187 | print(f'Loading dataloader') 188 | os.makedirs(config.samples_dir, exist_ok=True) 189 | 190 | eval_iterator = dataloader.SFTDataLoader( 191 | ['arenahard'], 192 | tokenizer, 193 | split='test', 194 | batch_size=config.model.eval_batch_size, 195 | n_examples=None, 196 | n_epochs=1, 197 | **data_iterator_kwargs 198 | ) 199 | 200 | trainer = BasicTrainer(tokenizer, config, None, eval_iterator, policy, reference_model=reference_model) 201 | samples = trainer.sample(include_original_prompt=True) 202 | alpaca_formatted_examples = [] 203 | 204 | for sample in samples: 205 | alpaca_formatted_examples.append({ 206 | 'instruction' : sample['original_prompt'], 207 | 'output': sample['policy'].strip(), 208 | 'reference' : sample['chosen'].strip(), 209 | }) 210 | 211 | fn = os.path.join(config.samples_dir, f'alpaca_{config.exp_name}.json') 212 | json.dump(alpaca_formatted_examples, open(fn, 'w'), indent=2) 213 | else: 214 | raise Exception("mode is neither sample nor eval") 215 | 216 | 217 | if __name__ == '__main__': 218 | main() 219 | -------------------------------------------------------------------------------- /feature_alignment/feature_map.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import tqdm 3 | import os 4 | from transformers import AutoTokenizer, AutoModelForCausalLM 5 | from datasets import load_dataset 6 | from huggingface_hub import hf_hub_download, login 7 | import numpy as np 8 | from .utils import disable_dropout 9 | 10 | 11 | @torch.no_grad() 12 | def get_feature_map( 13 | model_name_or_path: str, 14 | sae_encoder_name_or_path: int, 15 | sae_layer_id: int, 16 | temperature: float = 1.0, 17 | visualize: bool = True, 18 | cache_dir: str = ".cache", 19 | batch_size: int = 8, 20 | total_samples: int = 100, 21 | release: bool = True, 22 | ): 23 | # load safe.json 24 | # dataset = load_dataset("json", data_files="safe.json", split="train") 25 | # dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=8) 26 | 27 | # login with Hugging Face token 28 | login(token="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") 29 | 30 | if release: 31 | # path_to_params = hf_hub_download( 32 | # repo_id="google/gemma-scope-2b-pt-res", 33 | # filename="layer_25/width_16k/average_l0_55/params.npz", 34 | # force_download=False, 35 | # ) 36 | 37 | path_to_params = hf_hub_download( 38 | repo_id="google/gemma-scope-9b-pt-res", 39 | filename="layer_41/width_16k/average_l0_52/params.npz", 40 | force_download=False, 41 | ) 42 | 43 | params = np.load(path_to_params) 44 | pt_params = {k: torch.from_numpy(v).cuda() for k, v in params.items()} 45 | 46 | from transformers_model.modeling_gemma2 import JumpReLUSAE 47 | sae_encoder = JumpReLUSAE(params['W_enc'].shape[0], params['W_enc'].shape[1]) 48 | sae_encoder.load_state_dict(pt_params) 49 | 50 | # cache_file_chosen = os.path.join(cache_dir, f"{model_name_or_path}_layer_{sae_layer_id}_chosen_feature_map.pt") 51 | # cache_file_rejected = os.path.join(cache_dir, f"{model_name_or_path}_layer_{sae_layer_id}_rejected_feature_map.pt") 52 | 53 | # if os.path.exists(cache_file_chosen) and os.path.exists(cache_file_rejected): 54 | # print(f"Loading cached feature maps from {cache_file_chosen} and {cache_file_rejected}") 55 | # chosen_feature_map = torch.load(cache_file_chosen) 56 | # rejected_feature_map = torch.load(cache_file_rejected) 57 | # else: 58 | # if "gemma-2" in model_name_or_path: 59 | # from transformers_model.modeling_gemma2 import Gemma2ForCausalLM 60 | # model = AutoModelForCausalLM.from_pretrained( 61 | # model_name_or_path, 62 | # low_cpu_mem_usage=True, 63 | # ) 64 | # elif "Qwen1.5-0.5B" in model_name_or_path: 65 | # from transformers_model.modeling_qwen2 import Qwen2ForCausalLM 66 | # model = Qwen2ForCausalLM.from_pretrained( 67 | # model_name_or_path, 68 | # low_cpu_mem_usage=True, 69 | # ) 70 | # else: 71 | # raise NotImplementedError(f"Model {model_name_or_path} not supported") 72 | 73 | # tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") 74 | # tokenizer.pad_token_id = tokenizer.eos_token_id 75 | # # disable_dropout(model) 76 | 77 | # # model.model.layers[sae_layer_id].set_encoder(sae_encoder) 78 | 79 | # model.to(device) 80 | # model.eval() 81 | 82 | # chosen_feature_map = None 83 | # rejected_feature_map = None 84 | 85 | # for i, batch in tqdm.tqdm(enumerate(dataloader), desc="Getting Feature map"): 86 | # if i * batch_size > total_samples: 87 | # break 88 | 89 | # chosen = batch["chosen"] 90 | # rejected = batch["rejected"] 91 | 92 | # chosen = tokenizer( 93 | # chosen, 94 | # return_tensors="pt", 95 | # padding=True, 96 | # truncation=True, 97 | # max_length=1024, 98 | # ) 99 | 100 | # rejected = tokenizer( 101 | # rejected, 102 | # return_tensors="pt", 103 | # padding=True, 104 | # truncation=True, 105 | # max_length=1024, 106 | # ) 107 | 108 | # chosen['input_ids'] = chosen['input_ids'].to(device) 109 | # chosen['attention_mask'] = chosen['attention_mask'].to(device) 110 | # rejected['input_ids'] = rejected['input_ids'].to(device) 111 | # rejected['attention_mask'] = rejected['attention_mask'].to(device) 112 | 113 | # # get logits and test is nan 114 | # logits = model(**chosen, use_cache=False).logits 115 | # if torch.isnan(logits).any(): 116 | # raise ValueError("NaN in logits") 117 | 118 | # chosen_feature_acts_reference = model(**chosen, use_cache=False).feature_acts 119 | # rejected_feature_acts_reference = model(**rejected, use_cache=False).feature_acts 120 | 121 | # if chosen_feature_map is None: 122 | # chosen_feature_map = chosen_feature_acts_reference.mean(dim=[0, 1]).detach() 123 | # else: 124 | # chosen_feature_map += chosen_feature_acts_reference.mean(dim=[0, 1]).detach() 125 | 126 | # if rejected_feature_map is None: 127 | # rejected_feature_map = rejected_feature_acts_reference.mean(dim=[0, 1]).detach() 128 | # else: 129 | # rejected_feature_map += rejected_feature_acts_reference.mean(dim=[0, 1]).detach() 130 | 131 | # # check if nan in chosen_feature_map and rejected_feature_map 132 | # if torch.isnan(chosen_feature_map).any() or torch.isnan(rejected_feature_map).any(): 133 | # raise ValueError("NaN in chosen_feature_map or rejected_feature_map") 134 | 135 | # # chosen_feature_map = (chosen_feature_map / temperature).softmax(dim=-1) 136 | # # rejected_feature_map = (rejected_feature_map / temperature).softmax(dim=-1) 137 | # os.makedirs(cache_dir, exist_ok=True) 138 | # torch.save(chosen_feature_map, cache_file_chosen) 139 | # torch.save(rejected_feature_map, cache_file_rejected) 140 | # print(f"Feature maps saved to {cache_file_chosen} and {cache_file_rejected}") 141 | 142 | # # check if nan in chosen_feature_map and rejected_feature_map 143 | # if torch.isnan(chosen_feature_map).any() or torch.isnan(rejected_feature_map).any(): 144 | # raise ValueError("NaN in chosen_feature_map or rejected_feature_map") 145 | 146 | return sae_encoder 147 | 148 | -------------------------------------------------------------------------------- /feature_alignment/model/dpo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from typing import Dict, List, Union, Tuple 5 | import lightning as L 6 | from .model import BasicModel 7 | from ..utils.util import pad_to_length 8 | from .sft import get_batch_logps 9 | 10 | class DPOModel(BasicModel): 11 | def training_step(self, batch: Dict, batch_idx: int) -> torch.Tensor: 12 | 13 | loss, metrics = self.get_batch_metrics(batch, mode="train") 14 | self.log_dict(metrics, sync_dist=True) 15 | self.log("loss", loss, prog_bar=True, on_step=True) 16 | return loss 17 | 18 | """A trainer for any loss that uses paired preference, like DPO.""" 19 | def concatenated_inputs( 20 | self, 21 | batch: Dict[str, Union[List, torch.LongTensor]] 22 | ) -> Dict[str, torch.LongTensor]: 23 | """Concatenate the chosen and rejected inputs into a single tensor. The first half is chosen outputs, the second half is rejected. 24 | 25 | Args: 26 | batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length). 27 | 28 | Returns: 29 | A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'. 30 | """ 31 | max_length = max(batch['chosen_combined_input_ids'].shape[1], batch['rejected_combined_input_ids'].shape[1]) 32 | concatenated_batch = {} 33 | for k in batch: 34 | if k.startswith('chosen') and isinstance(batch[k], torch.Tensor): 35 | pad_value = -100 if 'labels' in k else 0 36 | concatenated_key = k.replace('chosen', 'concatenated') 37 | concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value) 38 | for k in batch: 39 | if k.startswith('rejected') and isinstance(batch[k], torch.Tensor): 40 | pad_value = -100 if 'labels' in k else 0 41 | concatenated_key = k.replace('rejected', 'concatenated') 42 | concatenated_batch[concatenated_key] = torch.cat(( 43 | concatenated_batch[concatenated_key], 44 | pad_to_length(batch[k], max_length, pad_value=pad_value), 45 | ), dim=0) 46 | return concatenated_batch 47 | 48 | def forward( 49 | self, 50 | model: nn.Module, 51 | batch: Dict[str, Union[List, torch.LongTensor]], 52 | average_log_prob=False, 53 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: 54 | """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. 55 | Return two tensors of shape (batch size), one of the chosen examples, another of the rejected ones. 56 | 57 | Returns: 58 | chosen_logps: log probabilities of chosen examples (should be batch size / 2 if data was read in correctly) 59 | rejected_logps: log probabilities of rejected examples (should be batch size / 2 if data was read in correctly) 60 | """ 61 | concatenated_batch = self.concatenated_inputs(batch) 62 | 63 | all_logits = model( 64 | concatenated_batch['concatenated_combined_input_ids'], 65 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], use_cache=(not self.is_mistral) 66 | ).logits.to(self.precision) 67 | 68 | all_logps = get_batch_logps( 69 | all_logits, 70 | concatenated_batch['concatenated_labels'], 71 | average_log_prob=average_log_prob 72 | ) 73 | 74 | chosen_logps = all_logps[:batch['chosen_combined_input_ids'].shape[0]] 75 | rejected_logps = all_logps[batch['chosen_combined_input_ids'].shape[0]:] 76 | return chosen_logps, rejected_logps, all_logits 77 | 78 | def get_batch_metrics(self, batch: Dict[str, Union[List, torch.LongTensor]], mode: str=None): 79 | """Compute the loss and other metrics for the given batch of inputs.""" 80 | metrics = {} 81 | if mode is None: mode = self.config.mode 82 | 83 | if self.reference_model is None: 84 | policy_chosen_logps, policy_rejected_logps = self.forward( 85 | self.policy, batch 86 | ) 87 | losses, chosen_rewards, rejected_rewards = self.loss( 88 | policy_chosen_logps, policy_rejected_logps 89 | ) 90 | else: 91 | policy_chosen_logps, policy_rejected_logps, all_logits = self.forward( 92 | self.policy, batch 93 | ) 94 | with torch.no_grad(): 95 | reference_chosen_logps, reference_rejected_logps, reference_all_logits = self.forward( 96 | self.reference_model, batch 97 | ) 98 | losses, chosen_rewards, rejected_rewards = self.loss( 99 | policy_chosen_logps, policy_rejected_logps, 100 | reference_chosen_logps, reference_rejected_logps 101 | ) 102 | 103 | chosen_rewards = chosen_rewards.float().detach() 104 | rejected_rewards = rejected_rewards.float().detach() 105 | policy_chosen_logps = policy_chosen_logps.float().detach() 106 | policy_rejected_logps = policy_rejected_logps.float().detach() 107 | 108 | # accuracy calculated on unpaired examples 109 | # (for apples-to-apples comparison with UnpairedPreferenceTrainer) 110 | reward_accuracies = ( 111 | chosen_rewards > rejected_rewards.flip(dims=[0]) 112 | ).float() 113 | metrics[f'rewards_{mode}/chosen'] = chosen_rewards 114 | metrics[f'rewards_{mode}/rejected'] = rejected_rewards 115 | metrics[f'rewards_{mode}/accuracies'] = reward_accuracies 116 | metrics[f'rewards_{mode}/margins'] = (chosen_rewards - rejected_rewards) 117 | metrics[f'logps_{mode}/rejected'] = policy_rejected_logps 118 | metrics[f'logps_{mode}/chosen'] = policy_chosen_logps 119 | metrics[f'loss/{mode}'] = losses.mean() 120 | 121 | return losses.mean(), metrics 122 | 123 | def loss( 124 | self, 125 | policy_chosen_logps: torch.FloatTensor, 126 | policy_rejected_logps: torch.FloatTensor, 127 | reference_chosen_logps: torch.FloatTensor, 128 | reference_rejected_logps: torch.FloatTensor 129 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: 130 | """Compute the DPO loss for a batch of policy and reference model log probabilities.""" 131 | 132 | pi_logratios = policy_chosen_logps - policy_rejected_logps 133 | ref_logratios = reference_chosen_logps - reference_rejected_logps 134 | logits = pi_logratios - ref_logratios 135 | 136 | losses = -F.logsigmoid(self.config.loss.beta * logits) 137 | chosen_rewards = self.config.loss.beta * ( 138 | policy_chosen_logps - reference_chosen_logps 139 | ).detach() 140 | rejected_rewards = self.config.loss.beta * ( 141 | policy_rejected_logps - reference_rejected_logps 142 | ).detach() 143 | 144 | return losses, chosen_rewards, rejected_rewards -------------------------------------------------------------------------------- /feature_alignment/model/fpo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from typing import Dict, List, Union, Tuple 5 | from ..utils.util import detach_float_metrics, instantiate 6 | from .dpo import DPOModel 7 | 8 | def fpo_get_batch_logps( 9 | logits: torch.FloatTensor, 10 | reference_logits: torch.FloatTensor, 11 | labels: torch.LongTensor, 12 | pi_fm: torch.FloatTensor = None, 13 | ref_fm: torch.FloatTensor = None, 14 | average_log_prob: bool = False, 15 | temperature: float = 1, 16 | k: int = 50, 17 | ): 18 | """Compute the kl divergence/log probabilities of the given labels under the given logits. 19 | 20 | Args: 21 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 22 | reference_logits: Logits of the reference model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 23 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 24 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 25 | 26 | Returns: 27 | Several tensors of shape (batch_size,) containing the average/sum kl divergence/log probabilities of the given labels under the given logits. 28 | """ 29 | 30 | assert logits.shape[:-1] == labels.shape 31 | assert reference_logits.shape[:-1] == labels.shape 32 | 33 | labels = labels[:, 1:].clone() 34 | logits = logits[:, :-1, :] 35 | reference_logits = reference_logits[:, :-1, :] 36 | loss_mask = (labels != -100) 37 | 38 | # dummy token; we'll ignore the losses on these tokens later 39 | labels[labels == -100] = 0 40 | 41 | vocab_logps = logits.log_softmax(-1) 42 | 43 | reference_vocab_ps = reference_logits.softmax(-1) 44 | reference_vocab_logps = reference_vocab_ps.log() 45 | 46 | per_position_kl = (reference_vocab_ps * (reference_vocab_logps - vocab_logps)).sum(-1) 47 | per_token_logps = torch.gather(vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 48 | per_reference_token_logps = torch.gather(reference_vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 49 | logps_margin = (per_token_logps).sum(-1) / loss_mask.sum(-1) - (per_reference_token_logps).sum(-1) / loss_mask.sum(-1) 50 | 51 | if pi_fm is not None: 52 | pi_fm = pi_fm[:, :-1, :] 53 | ref_fm = ref_fm[:, :-1, :] 54 | 55 | if pi_fm is not None: 56 | ref_fm = (ref_fm * loss_mask.unsqueeze(-1)).mean(dim=1) 57 | pi_fm = (pi_fm * loss_mask.unsqueeze(-1)).mean(dim=1) 58 | 59 | # # L2 Norm 60 | # ref_fm = ref_fm / ref_fm.norm(dim=-1, keepdim=True) 61 | # pi_fm = pi_fm / pi_fm.norm(dim=-1, keepdim=True) 62 | 63 | pi_fm, indices = torch.topk(pi_fm, k, dim=-1) 64 | ref_fm = torch.gather(ref_fm, dim=-1, index=indices) 65 | 66 | fm_sae = (ref_fm - pi_fm).pow(2).mean(-1) 67 | else: 68 | fm_sae = torch.zeros_like(per_position_kl).sum(-1) 69 | 70 | 71 | if average_log_prob: 72 | return (logps_margin * loss_mask).sum(-1) / loss_mask.sum(-1), \ 73 | (per_position_kl * loss_mask).sum(-1) / loss_mask.sum(-1), 74 | else: 75 | return logps_margin, \ 76 | (per_position_kl * loss_mask).sum(-1), \ 77 | fm_sae 78 | 79 | 80 | class FPOModel(DPOModel): 81 | 82 | def configure_sae(self): 83 | from feature_alignment.sae.jump_relu_sae import load_jump_relu_sae 84 | self.sae_encoder = load_jump_relu_sae(self.config) 85 | 86 | # freeze 87 | for param in self.sae_encoder.parameters(): 88 | param.requires_grad = False 89 | 90 | def loss( 91 | self, 92 | chosen_logps_margin: torch.FloatTensor, 93 | rejected_logps_margin: torch.FloatTensor, 94 | chosen_position_mse: torch.FloatTensor, 95 | rejected_position_mse: torch.FloatTensor, 96 | ) : 97 | """Compute the TDPO loss for a batch of policy and reference model log probabilities. 98 | 99 | Args: 100 | chosen_logps_margin: The difference of log probabilities between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 101 | rejected_logps_margin: The difference of log probabilities between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 102 | chosen_position_mse: The difference of sequential kl divergence between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 103 | rejected_position_mse: The difference of sequential kl divergence between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 104 | 105 | 106 | Returns: 107 | A tuple of two tensors: (losses, rewards). 108 | The losses tensor contains the TDPO loss for each example in the batch. 109 | The rewards tensors contain the rewards for response pair. 110 | """ 111 | 112 | chosen_values = chosen_logps_margin + chosen_position_mse 113 | rejected_values = rejected_logps_margin + rejected_position_mse 114 | 115 | chosen_rejected_logps_margin = chosen_logps_margin - rejected_logps_margin 116 | 117 | alpha = self.config.loss.alpha 118 | beta = self.config.loss.beta 119 | logits = chosen_rejected_logps_margin - \ 120 | alpha * (rejected_position_mse - chosen_position_mse.detach()) 121 | losses = -F.logsigmoid(beta * logits) 122 | 123 | chosen_rewards = beta * chosen_values.detach() 124 | rejected_rewards = beta * rejected_values.detach() 125 | 126 | return losses, chosen_rewards, rejected_rewards 127 | 128 | def forward(self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]], average_log_prob=False) -> Tuple[torch.FloatTensor, torch.FloatTensor]: 129 | """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. 130 | """ 131 | concatenated_batch = self.concatenated_inputs(batch) 132 | outputs = model( 133 | concatenated_batch['concatenated_combined_input_ids'], 134 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], 135 | use_cache=(not self.is_mistral), 136 | output_hidden_states=True, 137 | ) 138 | all_logits = outputs.logits.to(self.precision) 139 | all_fm = outputs.hidden_states[-1].to(self.precision) 140 | all_fm = self.sae_encoder.encode(all_fm) 141 | 142 | with torch.no_grad(): 143 | reference_outputs = self.reference_model( 144 | concatenated_batch['concatenated_combined_input_ids'], 145 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], 146 | use_cache=(not self.is_mistral), 147 | output_hidden_states=True, 148 | ) 149 | reference_all_logits = reference_outputs.logits.to(self.precision) 150 | reference_all_fm = reference_outputs.hidden_states[-1].to(self.precision) 151 | reference_all_fm = self.sae_encoder.encode(reference_all_fm) 152 | 153 | all_logps_margin, all_position_kl, all_fm_mse = fpo_get_batch_logps( 154 | all_logits, 155 | reference_all_logits, 156 | concatenated_batch['concatenated_labels'], 157 | all_fm, 158 | reference_all_fm, 159 | ) 160 | 161 | chosen_logps_margin = all_logps_margin[:batch['chosen_input_ids'].shape[0]] 162 | rejected_logps_margin = all_logps_margin[batch['chosen_input_ids'].shape[0]:] 163 | chosen_position_kl = all_position_kl[:batch['chosen_input_ids'].shape[0]] 164 | rejected_position_kl = all_position_kl[batch['chosen_input_ids'].shape[0]:] 165 | chosen_fm_mse = all_fm_mse[:batch['chosen_input_ids'].shape[0]] 166 | rejected_fm_mse = all_fm_mse[batch['chosen_input_ids'].shape[0]:] 167 | 168 | return chosen_logps_margin, rejected_logps_margin, chosen_position_kl, rejected_position_kl, chosen_fm_mse, rejected_fm_mse 169 | 170 | def get_batch_metrics(self, batch: Dict[str, Union[List, torch.LongTensor]], mode: str=None): 171 | """Compute the loss and other metrics for the given batch of inputs.""" 172 | metrics = {} 173 | if mode is None: mode = self.config.mode 174 | 175 | chosen_logps_margin, rejected_logps_margin, chosen_position_kl, rejected_position_kl, chosen_fm_mse, rejected_fm_mse \ 176 | = self.forward(self.policy, batch) 177 | 178 | losses, chosen_rewards, rejected_rewards = self.loss( 179 | chosen_logps_margin, 180 | rejected_logps_margin, 181 | chosen_fm_mse, 182 | rejected_fm_mse, 183 | ) 184 | 185 | # accuracy calculated on unpaired examples (for apples-to-apples comparison with UnpairedPreferenceTrainer) 186 | reward_accuracies = (chosen_rewards > rejected_rewards.flip(dims=[0])).float() 187 | 188 | fm_mse = (chosen_fm_mse - rejected_fm_mse).detach() 189 | losses = losses.mean() 190 | 191 | metrics[f'rewards_{mode}/chosen'] = chosen_rewards 192 | metrics[f'rewards_{mode}/rejected'] = rejected_rewards 193 | metrics[f'rewards_{mode}/accuracies'] = reward_accuracies 194 | metrics[f'rewards_{mode}/margins'] = (chosen_rewards - rejected_rewards) 195 | metrics[f'kl_{mode}/chosen'] = chosen_position_kl 196 | metrics[f'kl_{mode}/rejected'] = rejected_position_kl 197 | metrics[f'kl_{mode}/margin'] = (chosen_position_kl - rejected_position_kl) 198 | metrics[f'kl_{mode}/fm margin'] = fm_mse 199 | metrics[f'kl_{mode}/fm chosen'] = chosen_fm_mse 200 | metrics[f'kl_{mode}/fm rejected'] = rejected_fm_mse 201 | metrics[f'loss/{mode}'] = losses.clone() 202 | 203 | metrics = detach_float_metrics(metrics) 204 | 205 | return losses, metrics -------------------------------------------------------------------------------- /feature_alignment/model/model.py: -------------------------------------------------------------------------------- 1 | from typing import Dict 2 | 3 | import lightning as L 4 | import torch 5 | import torch.nn as nn 6 | from typing import Any, Dict, List, Tuple, Union 7 | from omegaconf import DictConfig 8 | from ..utils.util import instantiate 9 | 10 | class BasicModel(L.LightningModule): 11 | """ 12 | A `LightningModule` 13 | Docs: 14 | https://lightning.ai/docs/pytorch/latest/common/lightning_module.html 15 | """ 16 | 17 | def __init__(self, config: DictConfig): 18 | super().__init__() 19 | self.config = config 20 | self.is_mistral = False 21 | self.policy = None 22 | self.configuration() 23 | 24 | def on_train_start(self) -> None: 25 | # Get the rank of the current process after the trainer is attached 26 | pass 27 | 28 | # --------------------- forward function --------------------- 29 | 30 | def forward(self) -> torch.Tensor: 31 | """ 32 | This will be called if we directly call the model 33 | e.g. model(noisy_latents, timesteps) 34 | TODO: check if this is needed 35 | """ 36 | pass 37 | 38 | # ------ training / testing / validation / inference loop ------ 39 | 40 | def training_step(self, batch: Dict, batch_idx: int) -> torch.Tensor: 41 | pass 42 | 43 | def test_step(self, batch: Dict, batch_idx: int): 44 | pass 45 | 46 | def validation_step(self, batch: Dict, batch_idx: int): 47 | pass 48 | 49 | def predict_step(self, batch: Dict, batch_idx: int, dataloader_idx: int = None): 50 | pass 51 | 52 | # ------------------- configure everything ------------------- 53 | 54 | def configure_optimizers(self) -> Dict[str, Any]: 55 | optimizer = torch.optim.AdamW( 56 | self.parameters(), 57 | lr=self.config.optimizer.lr, 58 | weight_decay=self.config.optimizer.weight_decay, 59 | betas=(self.config.optimizer.adam_beta1, self.config.optimizer.adam_beta2), 60 | eps=self.config.optimizer.adam_epsilon, 61 | ) 62 | 63 | def lr_lambda(current_step): 64 | warmup_steps = self.config.optimizer.warmup_steps 65 | 66 | if current_step < warmup_steps: 67 | warmup_factor = current_step / warmup_steps 68 | return warmup_factor 69 | else: 70 | return 1 71 | 72 | lr_scheduler = { 73 | "scheduler": torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda), 74 | "interval": "step", 75 | "frequency": 1, 76 | } 77 | 78 | return [optimizer], [lr_scheduler] 79 | 80 | def configuration(self): 81 | """ 82 | Our customised configuration, for getting the scheduler, vae, etc. 83 | NOTICE: must be freezed models 84 | """ 85 | # precision config 86 | if "bf" in self.config.trainer.precision: 87 | self.precision = torch.bfloat16 88 | elif "fp16" in self.config.trainer.precision: 89 | self.precision = torch.float16 90 | else: self.precision = torch.float32 91 | 92 | self.configure_sae() 93 | 94 | def configure_sae(self): 95 | pass 96 | 97 | def configure_model(self): 98 | """ 99 | Get the trainable models. Don't use self.xxx = xxx in __init__ because 100 | this will result in initializing the model on all GPUs. 101 | docs: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/fsdp.html#speed-up-model-initialization 102 | """ 103 | # policy model / or just the model for sft 104 | if self.policy is None: 105 | self.policy = instantiate(self.config.model, instantiate_module=False) 106 | if self.config.model.hf_model_name_or_path is not None: 107 | self.policy = self.policy.from_pretrained(self.config.model.hf_model_name_or_path) 108 | self.policy.to(self.device).to(self.precision) 109 | else: raise ValueError("No model name or path provided") 110 | 111 | # reference model 112 | if self.config.loss.use_reference_model: 113 | self.reference_model = instantiate(self.config.model, instantiate_module=False) 114 | if self.config.model.hf_model_name_or_path is not None: 115 | self.reference_model = self.reference_model.from_pretrained(self.config.model.hf_model_name_or_path) 116 | self.reference_model.to(self.device).to(self.precision) 117 | else: raise ValueError("No model name or path provided") 118 | 119 | # freeze the reference model 120 | for param in self.reference_model.parameters(): 121 | param.requires_grad = False 122 | 123 | -------------------------------------------------------------------------------- /feature_alignment/model/sft.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from typing import Dict, List, Union 5 | import lightning as L 6 | from .model import BasicModel 7 | 8 | def get_batch_logps( 9 | logits: torch.FloatTensor, 10 | labels: torch.LongTensor, 11 | average_log_prob: bool = False, 12 | token_level: bool = False 13 | ) -> torch.FloatTensor: 14 | """Compute the log probabilities of the given labels under the given logits. 15 | 16 | Args: 17 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 18 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 19 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 20 | token_level: If true, return the token-level log probabilities (do not aggregate across tokens) 21 | 22 | Returns: 23 | The relevant log probabilities. Of shape (batch_size,) by default and shape (batch size, sequence length) if token_level. 24 | """ 25 | assert logits.shape[:-1] == labels.shape 26 | 27 | labels = labels[:, 1:].clone() 28 | logits = logits[:, :-1, :] 29 | loss_mask = (labels != -100) 30 | 31 | # dummy token; we'll ignore the losses on these tokens later 32 | labels[labels == -100] = 0 33 | distribution_logps = logits.log_softmax(-1) 34 | 35 | per_token_logps = torch.gather(distribution_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) 36 | 37 | if token_level: 38 | return (per_token_logps * loss_mask) 39 | elif average_log_prob: 40 | return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) 41 | else: 42 | return (per_token_logps * loss_mask).sum(-1) 43 | 44 | 45 | class SFTModel(BasicModel): 46 | 47 | def training_step(self, batch: Dict, batch_idx: int) -> torch.Tensor: 48 | 49 | metrics = self.get_batch_metrics(batch, mode="train") 50 | self.log_dict(metrics, sync_dist=True) 51 | self.log("loss", metrics["loss"], prog_bar=True, on_step=True) 52 | return metrics 53 | 54 | def get_batch_metrics( 55 | self, 56 | batch: Dict[str, Union[List, torch.LongTensor]], 57 | mode: str=None, 58 | ): 59 | """Compute the loss and other metrics for the given batch of inputs. 60 | 61 | Args: 62 | batch: dictionary of inputs for the batch (should contain 'target_attention_mask', 'target_input_input_ids', 63 | 'target_labels' where 'target' corresponds to the SFT example) 64 | mode: one of 'train', 'eval', 'sample' 65 | """ 66 | metrics = {} 67 | if mode is None: mode = self.config.mode 68 | 69 | policy_chosen_logits = self.policy( 70 | batch['target_combined_input_ids'].to(self.device), 71 | attention_mask=batch['target_combined_attention_mask'].to(self.device), 72 | use_cache=(not self.is_mistral) 73 | ).logits 74 | 75 | policy_chosen_logps = get_batch_logps( 76 | policy_chosen_logits, 77 | batch['target_labels'].to(self.device), 78 | average_log_prob=True 79 | ) 80 | loss = -policy_chosen_logps.mean() 81 | 82 | metrics['loss'] = loss 83 | metrics[f'logps_{mode}/chosen'] = policy_chosen_logps 84 | metrics[f'loss/{mode}'] = loss 85 | return metrics -------------------------------------------------------------------------------- /feature_alignment/model/simpo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from typing import Dict, List, Union, Tuple 5 | from ..utils.util import detach_float_metrics 6 | from .dpo import DPOModel, get_batch_logps 7 | 8 | class SimPOModel(DPOModel): 9 | def loss( 10 | self, 11 | chosen_logps_margin: torch.FloatTensor, 12 | rejected_logps_margin: torch.FloatTensor, 13 | ) : 14 | """Compute the TDPO loss for a batch of policy and reference model log probabilities. 15 | 16 | Args: 17 | chosen_logps_margin: The difference of log probabilities between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 18 | rejected_logps_margin: The difference of log probabilities between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 19 | chosen_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 20 | rejected_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 21 | 22 | 23 | Returns: 24 | A tuple of two tensors: (losses, rewards). 25 | The losses tensor contains the TDPO loss for each example in the batch. 26 | The rewards tensors contain the rewards for response pair. 27 | """ 28 | 29 | chosen_values = chosen_logps_margin 30 | rejected_values = rejected_logps_margin 31 | chosen_rejected_logps_margin = chosen_logps_margin - rejected_logps_margin 32 | 33 | alpha = self.config.loss.alpha 34 | beta = self.config.loss.beta 35 | gamma = self.config.loss.gamma 36 | logits = beta * (alpha * chosen_rejected_logps_margin - gamma) 37 | losses = -F.logsigmoid(logits) 38 | 39 | chosen_rewards = beta * chosen_values.detach() 40 | rejected_rewards = beta * rejected_values.detach() 41 | 42 | return losses, chosen_rewards, rejected_rewards 43 | 44 | def forward( 45 | self, 46 | model: nn.Module, 47 | batch: Dict[str, Union[List, torch.LongTensor]], 48 | average_log_prob=True, # simpo is always average log prob 49 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: 50 | """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. 51 | Return two tensors of shape (batch size), one of the chosen examples, another of the rejected ones. 52 | 53 | Returns: 54 | chosen_logps: log probabilities of chosen examples (should be batch size / 2 if data was read in correctly) 55 | rejected_logps: log probabilities of rejected examples (should be batch size / 2 if data was read in correctly) 56 | """ 57 | concatenated_batch = self.concatenated_inputs(batch) 58 | 59 | all_logits = model( 60 | concatenated_batch['concatenated_combined_input_ids'], 61 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], use_cache=(not self.is_mistral) 62 | ).logits.to(self.precision) 63 | 64 | all_logps = get_batch_logps( 65 | all_logits, 66 | concatenated_batch['concatenated_labels'], 67 | average_log_prob=average_log_prob 68 | ) 69 | 70 | chosen_logps = all_logps[:batch['chosen_combined_input_ids'].shape[0]] 71 | rejected_logps = all_logps[batch['chosen_combined_input_ids'].shape[0]:] 72 | return chosen_logps, rejected_logps, all_logits 73 | 74 | def get_batch_metrics(self, batch: Dict[str, Union[List, torch.LongTensor]], mode: str=None): 75 | """Compute the loss and other metrics for the given batch of inputs.""" 76 | metrics = {} 77 | if mode is None: mode = self.config.mode 78 | 79 | policy_chosen_logps, policy_rejected_logps, all_logits = self.forward( 80 | self.policy, batch 81 | ) 82 | 83 | with torch.no_grad(): 84 | reference_chosen_logps, reference_rejected_logps, reference_all_logits \ 85 | = self.forward( 86 | self.reference_model, batch 87 | ) 88 | 89 | losses, chosen_rewards, rejected_rewards = self.loss( 90 | policy_chosen_logps, policy_rejected_logps 91 | ) 92 | 93 | # accuracy calculated on unpaired examples (for apples-to-apples comparison with UnpairedPreferenceTrainer) 94 | reward_accuracies = ( 95 | chosen_rewards > rejected_rewards.flip(dims=[0]) 96 | ).float() 97 | losses = losses.mean() 98 | 99 | metrics[f'rewards_{mode}/chosen'] = chosen_rewards 100 | metrics[f'rewards_{mode}/rejected'] = rejected_rewards 101 | metrics[f'rewards_{mode}/accuracies'] = reward_accuracies 102 | metrics[f'rewards_{mode}/margins'] = (chosen_rewards - rejected_rewards) 103 | metrics[f'logps_{mode}/rejected'] = policy_rejected_logps 104 | metrics[f'logps_{mode}/chosen'] = policy_chosen_logps 105 | metrics[f'loss/{mode}'] = losses.clone() 106 | 107 | metrics = detach_float_metrics(metrics) 108 | 109 | return losses, metrics -------------------------------------------------------------------------------- /feature_alignment/model/tdpo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from typing import Dict, List, Union, Tuple 5 | from ..utils.util import detach_float_metrics 6 | from .dpo import DPOModel 7 | 8 | def tdpo_get_batch_logps( 9 | logits: torch.FloatTensor, 10 | reference_logits: torch.FloatTensor, 11 | labels: torch.LongTensor, 12 | average_log_prob: bool = False, 13 | ): 14 | """Compute the kl divergence/log probabilities of the given labels under the given logits. 15 | 16 | Args: 17 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 18 | reference_logits: Logits of the reference model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 19 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 20 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 21 | 22 | Returns: 23 | Several tensors of shape (batch_size,) containing the average/sum kl divergence/log probabilities of the given labels under the given logits. 24 | """ 25 | assert logits.shape[:-1] == labels.shape 26 | assert reference_logits.shape[:-1] == labels.shape 27 | 28 | labels = labels[:, 1:].clone() 29 | logits = logits[:, :-1, :] 30 | 31 | reference_logits = reference_logits[:, :-1, :] 32 | 33 | loss_mask = (labels != -100) 34 | 35 | # dummy token; we'll ignore the losses on these tokens later 36 | labels[labels == -100] = 0 37 | 38 | vocab_logps = logits.log_softmax(-1) 39 | 40 | reference_vocab_ps = reference_logits.softmax(-1) 41 | reference_vocab_logps = reference_vocab_ps.log() 42 | 43 | per_position_kl = (reference_vocab_ps * (reference_vocab_logps - vocab_logps)).sum(-1) 44 | per_token_logps = torch.gather(vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 45 | per_reference_token_logps = torch.gather(reference_vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 46 | logps_margin = per_token_logps - per_reference_token_logps 47 | 48 | if average_log_prob: 49 | return (logps_margin * loss_mask).sum(-1) / loss_mask.sum(-1), \ 50 | (per_position_kl * loss_mask).sum(-1) / loss_mask.sum(-1), \ 51 | (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) 52 | else: 53 | return (logps_margin * loss_mask).sum(-1), \ 54 | (per_position_kl * loss_mask).sum(-1), \ 55 | (per_token_logps * loss_mask).sum(-1), \ 56 | 57 | class TDPO1Model(DPOModel): 58 | """TDPO-1/2 Trainer.""" 59 | 60 | def loss( 61 | self, 62 | chosen_logps_margin: torch.FloatTensor, 63 | rejected_logps_margin: torch.FloatTensor, 64 | chosen_position_kl: torch.FloatTensor, 65 | rejected_position_kl: torch.FloatTensor 66 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: 67 | """Compute the TDPO loss for a batch of policy and reference model log probabilities. 68 | 69 | Args: 70 | chosen_logps_margin: The difference of log probabilities between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 71 | rejected_logps_margin: The difference of log probabilities between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 72 | chosen_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 73 | rejected_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 74 | 75 | 76 | Returns: 77 | A tuple of two tensors: (losses, rewards). 78 | The losses tensor contains the TDPO loss for each example in the batch. 79 | The rewards tensors contain the rewards for response pair. 80 | """ 81 | 82 | chosen_values = chosen_logps_margin + chosen_position_kl 83 | rejected_values = rejected_logps_margin + rejected_position_kl 84 | 85 | chosen_rejected_logps_margin = chosen_logps_margin - rejected_logps_margin 86 | 87 | logits = chosen_rejected_logps_margin - (rejected_position_kl - chosen_position_kl) 88 | beta = self.config.loss.beta 89 | losses = -F.logsigmoid(beta * logits) 90 | 91 | chosen_rewards = self.config.loss.beta * chosen_values.detach() 92 | rejected_rewards = beta * rejected_values.detach() 93 | 94 | return losses, chosen_rewards, rejected_rewards 95 | 96 | def forward( 97 | self, 98 | model: nn.Module, 99 | batch: Dict[str, Union[List, torch.LongTensor]], 100 | average_log_prob=False, 101 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: 102 | """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. 103 | """ 104 | concatenated_batch = self.concatenated_inputs(batch) 105 | all_logits = model( 106 | concatenated_batch['concatenated_combined_input_ids'], 107 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], 108 | use_cache=(not self.is_mistral) 109 | ).logits.to(self.precision) 110 | with torch.no_grad(): 111 | reference_all_logits = self.reference_model( 112 | concatenated_batch['concatenated_combined_input_ids'], 113 | attention_mask=concatenated_batch['concatenated_combined_attention_mask'], 114 | use_cache=(not self.is_mistral) 115 | ).logits.to(self.precision) 116 | 117 | all_logps_margin, all_position_kl, all_logps = tdpo_get_batch_logps( 118 | all_logits, 119 | reference_all_logits, 120 | concatenated_batch['concatenated_labels'], 121 | average_log_prob=False 122 | ) 123 | 124 | chosen_logps_margin = all_logps_margin[:batch['chosen_input_ids'].shape[0]] 125 | rejected_logps_margin = all_logps_margin[batch['chosen_input_ids'].shape[0]:] 126 | chosen_position_kl = all_position_kl[:batch['chosen_input_ids'].shape[0]] 127 | rejected_position_kl = all_position_kl[batch['chosen_input_ids'].shape[0]:] 128 | 129 | chosen_logps = all_logps[:batch['chosen_input_ids'].shape[0]].detach() 130 | rejected_logps = all_logps[batch['chosen_input_ids'].shape[0]:].detach() 131 | 132 | return chosen_logps_margin, rejected_logps_margin, chosen_position_kl, \ 133 | rejected_position_kl, chosen_logps, rejected_logps 134 | 135 | def get_batch_metrics(self, batch: Dict[str, Union[List, torch.LongTensor]], mode: str=None): 136 | """Compute the loss and other metrics for the given batch of inputs.""" 137 | metrics = {} 138 | if mode is None: mode = self.config.mode 139 | 140 | chosen_logps_margin, rejected_logps_margin, chosen_position_kl, rejected_position_kl, policy_chosen_logps, policy_rejected_logps\ 141 | = self.forward(self.policy, batch) 142 | losses, chosen_rewards, rejected_rewards = self.loss( 143 | chosen_logps_margin, 144 | rejected_logps_margin, 145 | chosen_position_kl, 146 | rejected_position_kl, 147 | ) 148 | 149 | losses = losses.mean() 150 | 151 | # accuracy calculated on unpaired examples (for apples-to-apples comparison with UnpairedPreferenceTrainer) 152 | reward_accuracies = ( 153 | chosen_rewards > rejected_rewards.flip(dims=[0]) 154 | ).float() 155 | 156 | metrics[f'rewards_{mode}/chosen'] = chosen_rewards 157 | metrics[f'rewards_{mode}/rejected'] = rejected_rewards 158 | metrics[f'rewards_{mode}/accuracies'] = reward_accuracies 159 | metrics[f'rewards_{mode}/margins'] = (chosen_rewards - rejected_rewards) 160 | metrics[f'logps_{mode}/rejected'] = policy_rejected_logps 161 | metrics[f'logps_{mode}/chosen'] = policy_chosen_logps 162 | metrics[f'loss/{mode}'] = losses.clone() 163 | metrics[f'kl_{mode}/chosen'] = chosen_position_kl 164 | metrics[f'kl_{mode}/rejected'] = rejected_position_kl 165 | metrics[f'kl_{mode}/margin'] = (chosen_position_kl - rejected_position_kl) 166 | 167 | metrics = detach_float_metrics(metrics) # detach and float 168 | 169 | return losses, metrics 170 | 171 | class TDPO2Model(TDPO1Model): 172 | def loss( 173 | self, 174 | chosen_logps_margin: torch.FloatTensor, 175 | rejected_logps_margin: torch.FloatTensor, 176 | chosen_position_kl: torch.FloatTensor, 177 | rejected_position_kl: torch.FloatTensor 178 | ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: 179 | """Compute the TDPO loss for a batch of policy and reference model log probabilities. 180 | 181 | Args: 182 | chosen_logps_margin: The difference of log probabilities between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 183 | rejected_logps_margin: The difference of log probabilities between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 184 | chosen_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the chosen responses. Shape: (batch_size,) 185 | rejected_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the rejected responses. Shape: (batch_size,) 186 | 187 | Returns: 188 | A tuple of two tensors: (losses, rewards). 189 | The losses tensor contains the TDPO loss for each example in the batch. 190 | The rewards tensors contain the rewards for response pair. 191 | """ 192 | 193 | chosen_values = chosen_logps_margin + chosen_position_kl 194 | rejected_values = rejected_logps_margin + rejected_position_kl 195 | 196 | chosen_rejected_logps_margin = chosen_logps_margin - rejected_logps_margin 197 | 198 | alpha = self.config.loss.alpha 199 | beta = self.config.loss.beta 200 | logits = chosen_rejected_logps_margin - alpha * (rejected_position_kl - chosen_position_kl.detach()) 201 | losses = -F.logsigmoid(beta * logits) 202 | 203 | chosen_rewards = beta * chosen_values.detach() 204 | rejected_rewards = beta * rejected_values.detach() 205 | 206 | return losses, chosen_rewards, rejected_rewards -------------------------------------------------------------------------------- /feature_alignment/models.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2023 Contextual AI, Inc. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Contains the classes necessary for doing PPO (offline, one-step) with language model. 8 | This code is largely from the TRL library, with some modifications to ensure stability. 9 | """ 10 | import json 11 | import os 12 | from copy import deepcopy 13 | 14 | import torch 15 | import torch.nn as nn 16 | from huggingface_hub import hf_hub_download 17 | from safetensors.torch import load_file as safe_load_file 18 | from transformers import PreTrainedModel, AutoModelForCausalLM 19 | 20 | 21 | LAYER_PATTERNS = ["transformer.h.{layer}", "model.decoder.layers.{layer}", "gpt_neox.layers.{layer}"] 22 | 23 | 24 | class PreTrainedModelWrapper(nn.Module): 25 | r""" 26 | A wrapper class around a (`transformers.PreTrainedModel`) to be compatible with the 27 | (`~transformers.PreTrained`) class in order to keep some attributes and methods of the 28 | (`~transformers.PreTrainedModel`) class. 29 | 30 | Attributes: 31 | pretrained_model: (`transformers.PreTrainedModel`) 32 | The model to be wrapped. 33 | parent_class: (`transformers.PreTrainedModel`) 34 | The parent class of the model to be wrapped. 35 | supported_args: (`list`) 36 | The list of arguments that are supported by the wrapper class. 37 | """ 38 | transformers_parent_class = None 39 | supported_args = None 40 | supported_modules = ("v_head",) 41 | 42 | def __init__(self, pretrained_model=None, **kwargs): 43 | super().__init__() 44 | self.pretrained_model = pretrained_model 45 | 46 | @classmethod 47 | def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): 48 | r""" 49 | Instantiates a new model from a pretrained model from `transformers`. The 50 | pretrained model is loaded using the `from_pretrained` method of the 51 | `transformers.PreTrainedModel` class. The arguments that are specific to the 52 | `transformers.PreTrainedModel` class are passed along this method and filtered 53 | out from the `kwargs` argument. 54 | 55 | 56 | Args: 57 | pretrained_model_name_or_path (`str` or `transformers.PreTrainedModel`): 58 | The path to the pretrained model or its name. 59 | *model_args (`list`, *optional*)): 60 | Additional positional arguments passed along to the underlying model's 61 | `from_pretrained` method. 62 | **kwargs (`dict`, *optional*): 63 | Additional keyword arguments passed along to the underlying model's 64 | `from_pretrained` method. 65 | """ 66 | if kwargs is not None: 67 | model_kwargs, pretrained_kwargs = cls._split_kwargs(kwargs) 68 | else: 69 | model_kwargs, pretrained_kwargs = {}, {} 70 | 71 | # First, load the pre-trained model using the parent-class 72 | # either `AutoModelForCausalLM` or `AutoModelForSeq2SeqLM` 73 | if isinstance(pretrained_model_name_or_path, str): 74 | pretrained_model = cls.transformers_parent_class.from_pretrained( 75 | pretrained_model_name_or_path, *model_args, **pretrained_kwargs 76 | ) 77 | elif isinstance(pretrained_model_name_or_path, PreTrainedModel): 78 | pretrained_model = pretrained_model_name_or_path 79 | else: 80 | raise ValueError( 81 | "pretrained_model_name_or_path should be a string or a PreTrainedModel, " 82 | f"but is {type(pretrained_model_name_or_path)}" 83 | ) 84 | # Then, create the full model by instantiating the wrapper class 85 | model = cls(pretrained_model, *model_args, **model_kwargs) 86 | 87 | # if resume_training, load the state_dict again - this is ok since the 88 | # state_dict is removed from the model after loading it. 89 | if isinstance(pretrained_model_name_or_path, str): 90 | filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin") 91 | safe_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors") 92 | sharded_index_filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin.index.json") 93 | safe_sharded_index_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json") 94 | is_shared = False 95 | use_safe = os.path.exists(safe_filename) 96 | 97 | if not (os.path.exists(filename) or os.path.exists(safe_filename)): 98 | try: 99 | try: 100 | filename = hf_hub_download(pretrained_model_name_or_path, "pytorch_model.bin") 101 | except: 102 | use_safe = True 103 | safe_filename = hf_hub_download(pretrained_model_name_or_path, "model.safetensors") 104 | # sharded 105 | except: # noqa 106 | if not use_safe: 107 | if os.path.exists(sharded_index_filename): 108 | index_file_name = sharded_index_filename 109 | else: 110 | index_file_name = hf_hub_download( 111 | pretrained_model_name_or_path, "pytorch_model.bin.index.json" 112 | ) 113 | else: 114 | if os.path.exists(safe_sharded_index_filename): 115 | index_file_name = safe_sharded_index_filename 116 | else: 117 | index_file_name = hf_hub_download( 118 | pretrained_model_name_or_path, "model.safetensors.index.json" 119 | ) 120 | # load json 121 | with open(index_file_name, "r") as f: 122 | index = json.load(f) 123 | # check filename with `v_head` or any known extra module: 124 | files_to_download = set() 125 | for k, v in index["weight_map"].items(): 126 | if any([module in k for module in cls.supported_modules]): 127 | files_to_download.add(v) 128 | is_shared = True 129 | 130 | loading_func = safe_load_file if use_safe else torch.load 131 | load_kwargs = {} if use_safe else {"map_location": "cpu"} 132 | 133 | if is_shared: 134 | # download each file and add it to the state_dict 135 | state_dict = {} 136 | for shard_file in files_to_download: 137 | filename = hf_hub_download(pretrained_model_name_or_path, shard_file) 138 | state_dict.update(loading_func(filename, **load_kwargs)) 139 | else: 140 | state_dict = loading_func(filename if not use_safe else safe_filename, **load_kwargs) 141 | 142 | else: 143 | state_dict = pretrained_model_name_or_path.state_dict() 144 | 145 | model.post_init(state_dict=state_dict) 146 | 147 | return model 148 | 149 | @classmethod 150 | def _split_kwargs(cls, kwargs): 151 | """ 152 | Separate the kwargs from the arguments that we support inside 153 | `supported_args` and the ones that we don't. 154 | """ 155 | supported_kwargs = {} 156 | unsupported_kwargs = {} 157 | 158 | for key, value in kwargs.items(): 159 | if key in cls.supported_args: 160 | supported_kwargs[key] = value 161 | else: 162 | unsupported_kwargs[key] = value 163 | 164 | return supported_kwargs, unsupported_kwargs 165 | 166 | def push_to_hub(self, *args, **kwargs): 167 | r""" 168 | Push the pretrained model to the hub. This method is a wrapper around 169 | `transformers.PreTrainedModel.push_to_hub`. Please refer to the documentation 170 | of `transformers.PreTrainedModel.push_to_hub` for more information. 171 | 172 | Args: 173 | *args (`list`, *optional*): 174 | Positional arguments passed along to the underlying model's 175 | `push_to_hub` method. 176 | **kwargs (`dict`, *optional*): 177 | Keyword arguments passed along to the underlying model's 178 | `push_to_hub` method. 179 | """ 180 | raise NotImplementedError 181 | 182 | def save_pretrained(self, *args, **kwargs): 183 | r""" 184 | Save the pretrained model to a directory. This method is a wrapper around 185 | `transformers.PreTrainedModel.save_pretrained`. Please refer to the documentation 186 | of `transformers.PreTrainedModel.save_pretrained` for more information. 187 | 188 | Args: 189 | *args (`list`, *optional*): 190 | Positional arguments passed along to the underlying model's 191 | `save_pretrained` method. 192 | **kwargs (`dict`, *optional*): 193 | Keyword arguments passed along to the underlying model's 194 | `save_pretrained` method. 195 | """ 196 | state_dict = kwargs.pop("state_dict", None) 197 | if state_dict is None: 198 | state_dict = self.state_dict() 199 | kwargs["state_dict"] = state_dict 200 | 201 | return self.pretrained_model.save_pretrained(*args, **kwargs) 202 | 203 | def state_dict(self, *args, **kwargs): 204 | r""" 205 | Return the state_dict of the pretrained model. 206 | """ 207 | raise NotImplementedError 208 | 209 | def post_init(self, *args, **kwargs): 210 | r""" 211 | Post initialization method. This method is called after the model is 212 | instantiated and loaded from a checkpoint. It can be used to perform 213 | additional operations such as loading the state_dict. 214 | """ 215 | raise NotImplementedError 216 | 217 | 218 | class ValueHead(nn.Module): 219 | r""" 220 | The ValueHead class implements a head for autoregressive that returns a scalar for each output token. 221 | The weights of the value head need to be in FP32. 222 | """ 223 | 224 | def __init__(self, config, **kwargs): 225 | super().__init__() 226 | if not hasattr(config, "summary_dropout_prob"): 227 | summary_dropout_prob = kwargs.pop("summary_dropout_prob", 0.1) 228 | else: 229 | summary_dropout_prob = config.summary_dropout_prob 230 | 231 | # some models such as OPT have a projection layer before the word embeddings - e.g. OPT-350m 232 | if hasattr(config, "word_embed_proj_dim"): 233 | hidden_size = config.word_embed_proj_dim 234 | else: 235 | hidden_size = config.hidden_size 236 | 237 | self.summary = nn.Sequential( 238 | nn.Linear(hidden_size, hidden_size), 239 | nn.Dropout(summary_dropout_prob) if summary_dropout_prob else nn.Identity(), 240 | nn.ReLU(), 241 | nn.Linear(hidden_size, hidden_size), 242 | nn.Dropout(summary_dropout_prob) if summary_dropout_prob else nn.Identity(), 243 | nn.ReLU(), 244 | nn.Linear(hidden_size, 1) 245 | ) 246 | self.flatten = nn.Flatten() 247 | 248 | def forward(self, hidden_states): 249 | # detach so that loss isn't backproped through LM 250 | # upcast since fp32 is important for good value predictions 251 | hidden_states = hidden_states.detach().to(torch.float32) 252 | output = self.summary(hidden_states) 253 | return output 254 | 255 | 256 | class AutoModelForCausalLMWithValueHead(PreTrainedModelWrapper): 257 | r""" 258 | An autoregressive model with a value head in addition to the language model head. 259 | 260 | Class attributes: 261 | - **transformers_parent_class** (`transformers.PreTrainedModel`) -- The parent class of the wrapped model. This 262 | should be set to `transformers.AutoModelForCausalLM` for this class. 263 | - **lm_head_namings** (`tuple`) -- A tuple of strings that are used to identify the language model head of the 264 | wrapped model. This is set to `("lm_head", "embed_out")` for this class but can be changed for other models 265 | in the future 266 | - **supported_args** (`tuple`) -- A tuple of strings that are used to identify the arguments that are supported 267 | by the `ValueHead` class. Currently, the supported args are: 268 | - **summary_dropout_prob** (`float`, `optional`, defaults to `None`) -- The dropout probability for the 269 | `ValueHead` class. 270 | - **v_head_initializer_range** (`float`, `optional`, defaults to `0.2`) -- The initializer range for the 271 | `ValueHead` if a specific initialization strategy is selected. 272 | - **v_head_init_strategy** (`str`, `optional`, defaults to `None`) -- The initialization strategy for the 273 | `ValueHead`. Currently, the supported strategies are: 274 | - **`None`** -- Initializes the weights of the `ValueHead` with a random distribution. This is the default 275 | strategy. 276 | - **"normal"** -- Initializes the weights of the `ValueHead` with a normal distribution. 277 | 278 | """ 279 | transformers_parent_class = AutoModelForCausalLM 280 | lm_head_namings = ["lm_head", "embed_out"] 281 | supported_args = ( 282 | "summary_dropout_prob", 283 | "v_head_initializer_range", 284 | "v_head_init_strategy", 285 | ) 286 | 287 | def __init__(self, pretrained_model, *args, **kwargs): 288 | r""" 289 | Initializes the model. 290 | 291 | Args: 292 | pretrained_model (`transformers.PreTrainedModel`): 293 | The model to wrap. It should be a causal language model such as GPT2. 294 | or any model mapped inside the `AutoModelForCausalLM` class. 295 | kwargs (`dict`, `optional`): 296 | Additional keyword arguments, that are passed to the `ValueHead` class. 297 | """ 298 | super().__init__(pretrained_model) 299 | v_head_kwargs, other_kwargs = self._split_kwargs(kwargs) 300 | 301 | if not any(hasattr(self.pretrained_model, attribute) for attribute in self.lm_head_namings): 302 | raise ValueError("The model does not have a language model head, please use a model that has one.") 303 | 304 | self.v_head = ValueHead(self.pretrained_model.config, **v_head_kwargs) 305 | self._init_weights(**v_head_kwargs) 306 | 307 | def _init_weights(self, **kwargs): 308 | r""" 309 | Initializes the weights of the value head. The default initialization strategy is random. 310 | Users can pass a different initialization strategy by passing the `v_head_init_strategy` argument 311 | when calling `.from_pretrained`. Supported strategies are: 312 | - `normal`: initializes the weights with a normal distribution. 313 | 314 | Args: 315 | **kwargs (`dict`, `optional`): 316 | Additional keyword arguments, that are passed to the `ValueHead` class. These arguments 317 | can contain the `v_head_init_strategy` argument as well as the `v_head_initializer_range` 318 | argument. 319 | """ 320 | initializer_range = kwargs.pop("v_head_initializer_range", 0.2) 321 | # random init by default 322 | init_strategy = kwargs.pop("v_head_init_strategy", None) 323 | if init_strategy is None: 324 | # do nothing 325 | pass 326 | elif init_strategy == "normal": 327 | def weights_init(m): 328 | if isinstance(m, nn.Linear): 329 | m.weight.data.normal_(mean=0.0, std=initializer_range) 330 | m.bias.data.zero_() 331 | 332 | self.summary.apply(weights_init) 333 | 334 | def forward( 335 | self, 336 | input_ids=None, 337 | past_key_values=None, 338 | attention_mask=None, 339 | **kwargs, 340 | ): 341 | r""" 342 | Applies a forward pass to the wrapped model and returns the logits of the value head. 343 | 344 | Args: 345 | input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): 346 | Indices of input sequence tokens in the vocabulary. 347 | past_key_values (`tuple(tuple(torch.FloatTensor))`, `optional`): 348 | Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model 349 | (see `past_key_values` input) to speed up sequential decoding. 350 | attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`): 351 | Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: 352 | - 1 for tokens that are **not masked**, 353 | - 0 for tokens that are **masked**. 354 | kwargs (`dict`, `optional`): 355 | Additional keyword arguments, that are passed to the wrapped model. 356 | """ 357 | kwargs["output_hidden_states"] = True # this had already been set in the LORA / PEFT examples 358 | kwargs["past_key_values"] = past_key_values 359 | 360 | base_model_output = self.pretrained_model( 361 | input_ids=input_ids, 362 | attention_mask=attention_mask, 363 | **kwargs, 364 | ) 365 | 366 | last_hidden_state = base_model_output.hidden_states[-1] 367 | lm_logits = base_model_output.logits 368 | loss = base_model_output.loss 369 | 370 | # force upcast in fp32 if logits are in half-precision 371 | if lm_logits.dtype != torch.float32: 372 | lm_logits = lm_logits.float() 373 | 374 | value = self.v_head(last_hidden_state).squeeze(-1) 375 | 376 | return (lm_logits, loss, value) 377 | 378 | def generate(self, *args, **kwargs): 379 | r""" 380 | A simple wrapper around the `generate` method of the wrapped model. 381 | Please refer to the [`generate`](https://huggingface.co/docs/transformers/internal/generation_utils) 382 | method of the wrapped model for more information about the supported arguments. 383 | 384 | Args: 385 | *args (`list`, *optional*): 386 | Positional arguments passed to the `generate` method of the wrapped model. 387 | **kwargs (`dict`, *optional*): 388 | Keyword arguments passed to the `generate` method of the wrapped model. 389 | """ 390 | return self.pretrained_model.generate(*args, **kwargs) 391 | 392 | def state_dict(self, *args, **kwargs): 393 | r""" 394 | Returns the state dictionary of the model. We add the state dictionary of the value head 395 | to the state dictionary of the wrapped model by prepending the key with `v_head.`. 396 | """ 397 | pretrained_model_state_dict = self.pretrained_model.state_dict(*args, **kwargs) 398 | 399 | v_head_state_dict = self.v_head.state_dict(*args, **kwargs) 400 | for k, v in v_head_state_dict.items(): 401 | pretrained_model_state_dict[f"v_head.{k}"] = v 402 | return pretrained_model_state_dict 403 | 404 | def push_to_hub(self, *args, **kwargs): 405 | setattr(self.pretrained_model, "v_head", self.v_head) 406 | return self.pretrained_model.push_to_hub(*args, **kwargs) 407 | 408 | def post_init(self, state_dict): 409 | r""" 410 | We add the state dictionary of the value head to the state dictionary of the wrapped model 411 | by prepending the key with `v_head.`. This function removes the `v_head.` prefix from the 412 | keys of the value head state dictionary. 413 | """ 414 | for k in list(state_dict.keys()): 415 | if "v_head." in k: 416 | state_dict[k.replace("v_head.", "")] = state_dict.pop(k) 417 | self.v_head.load_state_dict(state_dict, strict=False) 418 | del state_dict -------------------------------------------------------------------------------- /feature_alignment/push.py: -------------------------------------------------------------------------------- 1 | """ 2 | Script to push model to the hugging face hub in the loadable format. 3 | 4 | Typical use: 5 | 6 | python push.py -c $MODEL_PATH/config.yaml 7 | 8 | where config.yaml is generated during training. 9 | """ 10 | import transformers 11 | import torch 12 | import hydra 13 | from omegaconf import OmegaConf, DictConfig 14 | from typing import Optional, Set 15 | import json, os 16 | from jinja2 import Template, Environment, FileSystemLoader 17 | from io import BytesIO 18 | from huggingface_hub import HfApi 19 | 20 | import argparse 21 | parser = argparse.ArgumentParser() 22 | parser.add_argument('--config', '-c', help="saved config file", type=str) 23 | 24 | 25 | if __name__ == "__main__": 26 | """Main entry point for evaluating. Validates config, loads model(s), and kicks off worker process(es).""" 27 | args = parser.parse_args() 28 | config = OmegaConf.load(args.config) 29 | print(OmegaConf.to_yaml(config)) 30 | exp_name = config.exp_name 31 | if '+' in exp_name: exp_name = config.exp_name.replace('+', '-') 32 | repo = f'ContextualAI/{exp_name}' 33 | 34 | env = Environment(loader=FileSystemLoader("assets/")) 35 | template = env.get_template("model_readme.jinja") 36 | output = template.render(model=config.model.name_or_path, loss=config.loss.name.upper(), thumbnail="https://gist.github.com/assets/29318529/fe2d8391-dbd1-4b7e-9dc4-7cb97e55bc06") 37 | print(f'Pushing model card to {repo}') 38 | with open('assets/temp.md', 'w') as f: 39 | f.write(output) 40 | api = HfApi() 41 | api.upload_file( 42 | path_or_fileobj='assets/temp.md', 43 | path_in_repo="README.md", 44 | repo_id=repo, 45 | repo_type="model", 46 | ) 47 | os.remove('assets/temp.md') 48 | 49 | tokenizer_name_or_path = config.local_run_dir 50 | print(f'Loading tokenizer at {tokenizer_name_or_path}') 51 | tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_name_or_path) 52 | if tokenizer.pad_token_id is None: 53 | tokenizer.pad_token_id = tokenizer.eos_token_id 54 | print(f'Pushing tokenizer to {repo}') 55 | tokenizer.push_to_hub(repo, use_temp_dir=True, private=True) 56 | 57 | print('building policy') 58 | policy_dtype = getattr(torch, config.model.policy_dtype) 59 | policy = transformers.AutoModelForCausalLM.from_pretrained(config.model.name_or_path, low_cpu_mem_usage=True, torch_dtype=policy_dtype) 60 | # note that models were only resized for csft before saving 61 | # important because number of tokens in pretrained tokenizer is different from model.config.vocab_size, 62 | # so resizing at eval will throw an error if not resized before training 63 | if config.loss.name == 'csft': 64 | policy.resize_token_embeddings(len(tokenizer)) # model being loaded should already be trained with additional tokens for this to be valid 65 | 66 | state_dict = torch.load(os.path.join(config.cache_dir, config.saved_policy), map_location='cpu') 67 | step, metrics = state_dict['step_idx'], state_dict['metrics'] 68 | print(f'loading pre-trained weights at step {step} from {config.saved_policy} with metrics {json.dumps(metrics, indent=2)}') 69 | policy.load_state_dict(state_dict['state']) 70 | print(f'Pushing model to {repo}') 71 | policy.push_to_hub(repo, use_temp_dir=True, private=True) 72 | 73 | # check that the model can be loaded without problems 74 | try: 75 | print('loading model from hub') 76 | tokenizer = transformers.AutoTokenizer.from_pretrained(repo) 77 | policy = transformers.AutoModelForCausalLM.from_pretrained(repo, low_cpu_mem_usage=True, torch_dtype=policy_dtype) 78 | print('model loaded successfully') 79 | except: 80 | print(f'model failed to load from hub {repo}') 81 | -------------------------------------------------------------------------------- /feature_alignment/sae/jump_relu_sae.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch 3 | from huggingface_hub import hf_hub_download 4 | import numpy as np 5 | 6 | class JumpReLUSAE(nn.Module): 7 | def __init__(self, d_model, d_sae): 8 | # Note that we initialise these to zeros because we're loading in pre-trained weights. 9 | # If you want to train your own SAEs then we recommend using blah 10 | super().__init__() 11 | self.W_enc = nn.Parameter(torch.zeros(d_model, d_sae)) 12 | self.W_dec = nn.Parameter(torch.zeros(d_sae, d_model)) 13 | self.threshold = nn.Parameter(torch.zeros(d_sae)) 14 | self.b_enc = nn.Parameter(torch.zeros(d_sae)) 15 | self.b_dec = nn.Parameter(torch.zeros(d_model)) 16 | 17 | def encode(self, input_acts): 18 | pre_acts = input_acts @ self.W_enc + self.b_enc 19 | mask = (pre_acts > self.threshold) 20 | acts = mask * nn.functional.relu(pre_acts) 21 | return acts 22 | 23 | def decode(self, acts): 24 | return acts @ self.W_dec + self.b_dec 25 | 26 | def forward(self, acts): 27 | acts = self.encode(acts) 28 | recon = self.decode(acts) 29 | return recon 30 | 31 | def load_jump_relu_sae(config): 32 | path_to_params = hf_hub_download( 33 | repo_id=config.sae.sae_name_or_path, 34 | filename=config.sae.filename, 35 | force_download=False, 36 | ) 37 | 38 | params = np.load(path_to_params) 39 | pt_params = {k: torch.from_numpy(v).cuda() for k, v in params.items()} 40 | sae_model = JumpReLUSAE(params['W_enc'].shape[0], params['W_enc'].shape[1]) 41 | sae_model.load_state_dict(pt_params) 42 | 43 | if not config.sae.encoder: 44 | sae_model.W_enc = None 45 | sae_model.b_enc = None 46 | if not config.sae.decoder: 47 | sae_model.W_dec = None 48 | sae_model.b_dec = None 49 | 50 | return sae_model 51 | -------------------------------------------------------------------------------- /feature_alignment/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MikaStars39/FeatureAlignment/296e6a10c7c534cc787104c7c82832048e1685f9/feature_alignment/utils/__init__.py -------------------------------------------------------------------------------- /feature_alignment/utils/callbacks.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import lightning as L 4 | from lightning.pytorch.utilities import rank_zero_info, rank_zero_only 5 | 6 | 7 | class BasicCallback(L.Callback): 8 | def __init__(self): 9 | super().__init__() 10 | 11 | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): 12 | real_step = trainer.global_step # + config.epoch_begin * config.epoch_steps 13 | # TODO 14 | t_now = time.time_ns() 15 | kt_s = 0 16 | try: 17 | t_cost = (t_now - trainer.my_time_ns) / 1e9 18 | self.log("REAL it/s", 1.0 / t_cost, prog_bar=True, on_step=True) 19 | except: 20 | pass 21 | 22 | for param_group in trainer.optimizers[0].param_groups: 23 | lr = param_group["lr"] 24 | break 25 | 26 | trainer.my_lr = lr 27 | 28 | trainer.my_time_ns = t_now 29 | self.log("lr", lr, prog_bar=True, on_step=True) 30 | self.log("step", int(real_step), prog_bar=False, on_step=True) 31 | -------------------------------------------------------------------------------- /feature_alignment/utils/util.py: -------------------------------------------------------------------------------- 1 | from importlib import import_module 2 | from omegaconf import DictConfig 3 | import os 4 | import getpass 5 | from datetime import datetime 6 | import torch 7 | import random 8 | import numpy as np 9 | import torch.distributed as dist 10 | import inspect 11 | import importlib.util 12 | import socket 13 | import os 14 | from typing import Dict, Union, Type, List 15 | from collections.abc import Mapping 16 | 17 | def detach_float_metrics(metrics: Dict[str, torch.Tensor]) -> Dict[str, float]: 18 | for k, v in metrics.items(): 19 | metrics[k] = v.float().detach() 20 | return metrics 21 | 22 | def instantiate(config: DictConfig, instantiate_module=True): 23 | """Get arguments from config.""" 24 | module = import_module(config.module_name) 25 | class_ = getattr(module, config.class_name) 26 | if instantiate_module: 27 | init_args = {k: v for k, v in config.items() if k not in ["module_name", "class_name"]} 28 | return class_(**init_args) 29 | else: 30 | return class_ 31 | 32 | 33 | def deepcopy_fsdp_models(src, tgt): 34 | """Given two models src and tgt, copy every parameter from the src to the tgt model.""" 35 | with torch.no_grad(): 36 | src_params = { k: v for k,v in src.named_parameters() } 37 | tgt_params = { k: v for k,v in tgt.named_parameters() } 38 | 39 | for k in tgt_params: 40 | if k in src_params: 41 | tgt_params[k].data.copy_(src_params[k].data.detach()) 42 | else: 43 | rank0_print(f"{k} not found") 44 | 45 | 46 | def get_open_port(): 47 | with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: 48 | s.bind(('', 0)) # bind to all interfaces and use an OS provided port 49 | return s.getsockname()[1] # return only the port number 50 | 51 | 52 | def get_remote_file(remote_path, local_path=None): 53 | hostname, path = remote_path.split(':') 54 | local_hostname = socket.gethostname() 55 | if hostname == local_hostname or hostname == local_hostname[:local_hostname.find('.')]: 56 | return path 57 | 58 | if local_path is None: 59 | local_path = path 60 | # local_path = local_path.replace('/scr-ssd', '/scr') 61 | if os.path.exists(local_path): 62 | return local_path 63 | local_dir = os.path.dirname(local_path) 64 | os.makedirs(local_dir, exist_ok=True) 65 | 66 | print(f'Copying {hostname}:{path} to {local_path}') 67 | os.system(f'scp {remote_path} {local_path}') 68 | return local_path 69 | 70 | 71 | def rank0_print(*args, **kwargs): 72 | """Print, but only on rank 0.""" 73 | if not dist.is_initialized() or dist.get_rank() == 0: 74 | print(*args, **kwargs) 75 | 76 | 77 | def on_rank0(): 78 | return (not dist.is_initialized()) or (dist.get_rank() == 0) 79 | 80 | 81 | def slice_and_move_batch_for_device(batch: Dict, rank: int, world_size: int, device: str) -> Dict: 82 | """Slice a batch into chunks, and move each chunk to the specified device.""" 83 | chunk_size = len(list(batch.values())[0]) // world_size 84 | start = chunk_size * rank 85 | end = chunk_size * (rank + 1) 86 | sliced = {k: v[start:end] for k, v in batch.items()} 87 | on_device = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in sliced.items()} 88 | return on_device 89 | 90 | 91 | def pad_to_length(tensor: torch.Tensor, length: int, pad_value: Union[int, float], dim: int = -1) -> torch.Tensor: 92 | if tensor.size(dim) >= length: 93 | return tensor 94 | else: 95 | pad_size = list(tensor.shape) 96 | pad_size[dim] = length - tensor.size(dim) 97 | return torch.cat([tensor, pad_value * torch.ones(*pad_size, dtype=tensor.dtype, device=tensor.device)], dim=dim) 98 | 99 | 100 | def get_batch_logps(logits: torch.FloatTensor, labels: torch.LongTensor, average_log_prob: bool = False, token_level: bool = False): 101 | """Compute the log probabilities of the given labels under the given logits. 102 | 103 | Args: 104 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 105 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 106 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 107 | token_level: If true, return the token-level log probabilities (do not aggregate across tokens) 108 | 109 | Returns: 110 | The relevant log probabilities. Of shape (batch_size,) by default and shape (batch size, sequence length) if token_level. 111 | """ 112 | assert logits.shape[:-1] == labels.shape 113 | 114 | labels = labels[:, 1:].clone() 115 | logits = logits[:, :-1, :] 116 | loss_mask = (labels != -100) 117 | 118 | # dummy token; we'll ignore the losses on these tokens later 119 | labels[labels == -100] = 0 120 | distribution_logps = logits.log_softmax(-1) 121 | 122 | per_token_logps = torch.gather(distribution_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) 123 | 124 | if token_level: 125 | return (per_token_logps * loss_mask) 126 | elif average_log_prob: 127 | return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) 128 | else: 129 | return (per_token_logps * loss_mask).sum(-1) 130 | 131 | def tdpo_get_batch_logps( 132 | logits: torch.FloatTensor, 133 | reference_logits: torch.FloatTensor, 134 | labels: torch.LongTensor, 135 | average_log_prob: bool = False, 136 | ): 137 | """Compute the kl divergence/log probabilities of the given labels under the given logits. 138 | 139 | Args: 140 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 141 | reference_logits: Logits of the reference model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 142 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 143 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 144 | 145 | Returns: 146 | Several tensors of shape (batch_size,) containing the average/sum kl divergence/log probabilities of the given labels under the given logits. 147 | """ 148 | assert logits.shape[:-1] == labels.shape 149 | assert reference_logits.shape[:-1] == labels.shape 150 | 151 | labels = labels[:, 1:].clone() 152 | logits = logits[:, :-1, :] 153 | 154 | reference_logits = reference_logits[:, :-1, :] 155 | 156 | loss_mask = (labels != -100) 157 | 158 | # dummy token; we'll ignore the losses on these tokens later 159 | labels[labels == -100] = 0 160 | 161 | vocab_logps = logits.log_softmax(-1) 162 | 163 | reference_vocab_ps = reference_logits.softmax(-1) 164 | reference_vocab_logps = reference_vocab_ps.log() 165 | 166 | per_position_kl = (reference_vocab_ps * (reference_vocab_logps - vocab_logps)).sum(-1) 167 | per_token_logps = torch.gather(vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 168 | per_reference_token_logps = torch.gather(reference_vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 169 | logps_margin = per_token_logps - per_reference_token_logps 170 | 171 | if average_log_prob: 172 | return (logps_margin * loss_mask).sum(-1) / loss_mask.sum(-1), \ 173 | (per_position_kl * loss_mask).sum(-1) / loss_mask.sum(-1), \ 174 | (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) 175 | else: 176 | return (logps_margin * loss_mask).sum(-1), \ 177 | (per_position_kl * loss_mask).sum(-1), \ 178 | (per_token_logps * loss_mask).sum(-1), \ 179 | 180 | 181 | def tdpo_kl_get_batch_logps( 182 | logits: torch.FloatTensor, 183 | reference_logits: torch.FloatTensor, 184 | labels: torch.LongTensor, 185 | pi_fm: torch.FloatTensor = None, 186 | ref_fm: torch.FloatTensor = None, 187 | average_log_prob: bool = False, 188 | temperature: float = 1, 189 | k: int = 50, 190 | use_mse: bool = False, 191 | ): 192 | """Compute the kl divergence/log probabilities of the given labels under the given logits. 193 | 194 | Args: 195 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 196 | reference_logits: Logits of the reference model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 197 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 198 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 199 | 200 | Returns: 201 | Several tensors of shape (batch_size,) containing the average/sum kl divergence/log probabilities of the given labels under the given logits. 202 | """ 203 | 204 | assert logits.shape[:-1] == labels.shape 205 | assert reference_logits.shape[:-1] == labels.shape 206 | 207 | labels = labels[:, 1:].clone() 208 | logits = logits[:, :-1, :] 209 | reference_logits = reference_logits[:, :-1, :] 210 | loss_mask = (labels != -100) 211 | 212 | # dummy token; we'll ignore the losses on these tokens later 213 | labels[labels == -100] = 0 214 | 215 | vocab_logps = logits.log_softmax(-1) 216 | 217 | reference_vocab_ps = reference_logits.softmax(-1) 218 | reference_vocab_logps = reference_vocab_ps.log() 219 | 220 | per_position_kl = (reference_vocab_ps * (reference_vocab_logps - vocab_logps)).sum(-1) 221 | per_token_logps = torch.gather(vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 222 | per_reference_token_logps = torch.gather(reference_vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) * loss_mask 223 | logps_margin = (per_token_logps).sum(-1) / loss_mask.sum(-1) - (per_reference_token_logps).sum(-1) / loss_mask.sum(-1) 224 | 225 | if pi_fm is not None: 226 | pi_fm = pi_fm[:, :-1, :] 227 | ref_fm = ref_fm[:, :-1, :] 228 | 229 | if pi_fm is not None: 230 | ref_fm = (ref_fm * loss_mask.unsqueeze(-1)).mean(dim=1) 231 | pi_fm = (pi_fm * loss_mask.unsqueeze(-1)).mean(dim=1) 232 | 233 | # # L2 Norm 234 | # ref_fm = ref_fm / ref_fm.norm(dim=-1, keepdim=True) 235 | # pi_fm = pi_fm / pi_fm.norm(dim=-1, keepdim=True) 236 | 237 | pi_fm, indices = torch.topk(pi_fm, k, dim=-1) 238 | ref_fm = torch.gather(ref_fm, dim=-1, index=indices) 239 | 240 | fm_kl = (ref_fm - pi_fm).pow(2).mean(-1) 241 | else: 242 | fm_kl = torch.zeros_like(per_position_kl).sum(-1) 243 | 244 | 245 | if average_log_prob: 246 | return (logps_margin * loss_mask).sum(-1) / loss_mask.sum(-1), \ 247 | (per_position_kl * loss_mask).sum(-1) / loss_mask.sum(-1), 248 | else: 249 | return logps_margin, \ 250 | (per_position_kl * loss_mask).sum(-1), \ 251 | fm_kl 252 | 253 | 254 | def fdpo_kl_get_batch_logps( 255 | logits: torch.FloatTensor, 256 | reference_logits: torch.FloatTensor, 257 | labels: torch.LongTensor, 258 | pi_fm: torch.FloatTensor = None, 259 | ref_fm: torch.FloatTensor = None, 260 | average_log_prob: bool = False, 261 | temperature: float = 1, 262 | k: int = 100, 263 | ): 264 | """Compute the kl divergence/log probabilities of the given labels under the given logits. 265 | 266 | Args: 267 | logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 268 | reference_logits: Logits of the reference model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) 269 | labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length) 270 | average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. 271 | 272 | Returns: 273 | Several tensors of shape (batch_size,) containing the average/sum kl divergence/log probabilities of the given labels under the given logits. 274 | """ 275 | assert logits.shape[:-1] == labels.shape 276 | assert reference_logits.shape[:-1] == labels.shape 277 | 278 | labels = labels[:, 1:].clone() 279 | logits = logits[:, :-1, :] 280 | pi_fm = pi_fm[:, :-1, :] 281 | ref_fm = ref_fm[:, :-1, :] 282 | 283 | reference_logits = reference_logits[:, :-1, :] 284 | 285 | loss_mask = (labels != -100) 286 | 287 | # dummy token; we'll ignore the losses on these tokens later 288 | labels[labels == -100] = 0 289 | 290 | vocab_logps = logits.log_softmax(-1) 291 | 292 | reference_vocab_ps = reference_logits.softmax(-1) 293 | reference_vocab_logps = reference_vocab_ps.log() 294 | 295 | per_position_kl = (reference_vocab_ps * (reference_vocab_logps - vocab_logps)).sum(-1) 296 | per_token_logps = torch.gather(vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) 297 | per_reference_token_logps = torch.gather(reference_vocab_logps, dim=2, index=labels.unsqueeze(2)).squeeze(2) 298 | logps_margin = per_token_logps - per_reference_token_logps 299 | 300 | # select the top k elemets in ref_fm amd pi_fm 301 | pi_fm, indices = torch.topk(pi_fm, k, dim=-1) 302 | ref_fm = torch.gather(ref_fm, dim=-1, index=indices) 303 | 304 | ref_fm_ps = (ref_fm / temperature).softmax(-1) 305 | pi_fm_ps = ((pi_fm * ref_fm_ps / temperature).softmax(-1) + 1e-4).log() 306 | ref_fm_logps = (ref_fm_ps + 1e-4).log() 307 | 308 | 309 | if average_log_prob: 310 | return (logps_margin * loss_mask).sum(-1) / loss_mask.sum(-1), \ 311 | (per_position_kl * loss_mask).sum(-1) / loss_mask.sum(-1), \ 312 | (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) 313 | else: 314 | return (logps_margin * loss_mask).sum(-1), \ 315 | (per_position_kl * loss_mask).sum(-1), \ 316 | (fm_margin * loss_mask).sum(-1), \ 317 | (fm_kl * loss_mask).sum(-1) 318 | 319 | 320 | def clip_by_value(x, tensor_min, tensor_max): 321 | """ 322 | Tensor extenstion to torch.clamp 323 | https://github.com/pytorch/pytorch/issues/2793#issuecomment-428784713 324 | """ 325 | clipped = torch.max(torch.min(x, tensor_max), tensor_min) 326 | return clipped 327 | 328 | 329 | def masked_mean(values, mask, axis=None): 330 | """Compute mean of tensor with a masked values.""" 331 | if axis is not None: 332 | return (values * mask).sum(axis=axis) / mask.sum(axis=axis) 333 | else: 334 | return (values * mask).sum() / mask.sum() 335 | 336 | 337 | def masked_var(values, mask, unbiased=True): 338 | """Compute variance of tensor with masked values.""" 339 | mean = masked_mean(values, mask) 340 | centered_values = values - mean 341 | variance = masked_mean(centered_values**2, mask) 342 | return variance 343 | 344 | 345 | def rowwise_product(mat: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: 346 | """ 347 | Calculate the row-wise product over all the elements that have not been masked out. 348 | 349 | Args: 350 | mat: tensor of shape (batch_size, sequence length) 351 | mask: tensor of shape (batch_size, sequence length) 352 | 353 | Returns: 354 | Matrix of batch size. 355 | """ 356 | mat = mat.clone() 357 | indices = (mask == 0).long().nonzero() 358 | mat[indices[:,0], indices[:,1]] = 1 359 | return mat.prod(dim=1) 360 | 361 | 362 | def entropy_from_logits(logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: 363 | """Calculate entropy from logits. 364 | 365 | Args: 366 | logits: tensor of shape (batch_size, sequence length, vocab) 367 | mask: tensor of shape (batch_size, sequence length) 368 | 369 | Returns: 370 | The average tokenwise entropy across all non-masked tokens (of shape (1,)). 371 | """ 372 | pd = torch.nn.functional.softmax(logits, dim=-1) 373 | entropy = masked_mean(torch.logsumexp(logits, axis=-1) - torch.sum(pd * logits, axis=-1), mask) 374 | return entropy 375 | 376 | 377 | def flatten_dict(nested, sep="/"): 378 | """Flatten dictionary and concatenate nested keys with separator.""" 379 | 380 | def rec(nest, prefix, into): 381 | for k, v in nest.items(): 382 | if sep in k: 383 | raise ValueError(f"separator '{sep}' not allowed to be in key '{k}'") 384 | if isinstance(v, Mapping): 385 | rec(v, prefix + k + sep, into) 386 | else: 387 | into[prefix + k] = v 388 | 389 | flat = {} 390 | rec(nested, "", flat) 391 | return flat 392 | 393 | 394 | def all_gather_if_needed(values: torch.Tensor, rank: int, world_size: int) -> torch.Tensor: 395 | """Gather and stack/cat values from all processes, if there are multiple processes.""" 396 | if world_size == 1: 397 | return values 398 | 399 | device = torch.device('cuda', rank) 400 | all_values = [torch.empty_like(values).to(device) for _ in range(world_size)] 401 | dist.all_gather(all_values, values) 402 | cat_function = torch.cat if values.dim() > 0 else torch.stack 403 | return cat_function(all_values, dim=0) 404 | 405 | 406 | def formatted_dict(d: Dict) -> Dict: 407 | """Format a dictionary for printing.""" 408 | return {k: (f"{v:.5g}" if type(v) == float else v) for k, v in d.items()} 409 | 410 | 411 | def disable_dropout(model: torch.nn.Module): 412 | """Disable dropout in a model.""" 413 | for module in model.modules(): 414 | if isinstance(module, torch.nn.Dropout): 415 | module.p = 0 416 | 417 | 418 | def delete_dict(d: Dict): 419 | """Delete all items inside the dict.""" 420 | for k in list(d.keys()): 421 | del d[k] 422 | 423 | 424 | def print_gpu_memory(rank: int = None, message: str = ''): 425 | """Print the amount of GPU memory currently allocated for each GPU.""" 426 | if torch.cuda.is_available(): 427 | device_count = torch.cuda.device_count() 428 | for i in range(device_count): 429 | device = torch.device(f'cuda:{i}') 430 | allocated_bytes = torch.cuda.memory_allocated(device) 431 | if allocated_bytes == 0: 432 | continue 433 | print('*' * 40) 434 | print(f'[{message} rank {rank} ] GPU {i}: {allocated_bytes / 1024**2:.2f} MB') 435 | print('*' * 40) 436 | 437 | 438 | def get_block_class_from_model(model: torch.nn.Module, block_class_name: str) -> torch.nn.Module: 439 | """Get the class of a block from a model, using the block's class name.""" 440 | for module in model.modules(): 441 | if module.__class__.__name__ == block_class_name: 442 | return module.__class__ 443 | raise ValueError(f"Could not find block class {block_class_name} in model {model}") 444 | 445 | 446 | def get_block_class_from_model_class_and_block_name(model_class: Type, block_class_name: str) -> Type: 447 | filepath = inspect.getfile(model_class) 448 | assert filepath.endswith('.py'), f"Expected a .py file, got {filepath}" 449 | assert os.path.exists(filepath), f"File {filepath} does not exist" 450 | assert "transformers" in filepath, f"Expected a transformers model, got {filepath}" 451 | 452 | module_name = filepath[filepath.find('transformers'):].replace('/', '.')[:-3] 453 | print(f"Searching in file {filepath}, module {module_name} for class {block_class_name}") 454 | 455 | # Load the module dynamically 456 | spec = importlib.util.spec_from_file_location(module_name, filepath) 457 | module = importlib.util.module_from_spec(spec) 458 | spec.loader.exec_module(module) 459 | 460 | # Get the class dynamically 461 | class_ = getattr(module, block_class_name) 462 | print(f"Found class {class_} in module {module_name}") 463 | return class_ 464 | 465 | 466 | def init_distributed(rank: int, world_size: int, master_addr: str = 'localhost', port: int = 12355, backend: str = 'nccl'): 467 | print(rank, 'initializing distributed') 468 | os.environ["MASTER_ADDR"] = master_addr 469 | os.environ["MASTER_PORT"] = str(port) 470 | os.environ["CUDA_VISIBLE_DEVICES"] = str(rank) 471 | torch.cuda.set_device(rank) 472 | dist.init_process_group(backend, rank=rank, world_size=world_size) 473 | -------------------------------------------------------------------------------- /feature_alignment/visualize.py: -------------------------------------------------------------------------------- 1 | import os 2 | from openai import OpenAI 3 | 4 | client = OpenAI( 5 | # This is the default and can be omitted 6 | api_key="sk-XsCVDLd3COd5LTGcC89c09393cE444C1A1C8A6Cf2fF1D3B2", 7 | ) 8 | 9 | chat_completion = client.chat.completions.create( 10 | messages=[ 11 | { 12 | "role": "user", 13 | "content": "Say this is a test", 14 | } 15 | ], 16 | model="gpt-3.5-turbo", 17 | ) 18 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate==0.33.0 2 | aiohappyeyeballs==2.3.4 3 | aiohttp==3.10.0 4 | aiosignal==1.3.1 5 | annotated-types==0.7.0 6 | antlr4-python3-runtime==4.9.3 7 | anyio==4.4.0 8 | async-timeout==4.0.3 9 | attrs==24.1.0 10 | automated-interpretability==0.0.5 11 | babe==0.0.7 12 | beartype==0.14.1 13 | better-abc==0.0.3 14 | blobfile==2.1.1 15 | boostedblob==0.15.4 16 | certifi==2024.7.4 17 | charset-normalizer==3.3.2 18 | click==8.1.7 19 | config2py==0.1.36 20 | contourpy==1.2.1 21 | cycler==0.12.1 22 | datasets==2.20.0 23 | deepspeed==0.14.4 24 | dill==0.3.8 25 | distlib==0.3.8 26 | docker-pycreds==0.4.0 27 | docstring_parser==0.16 28 | dol==0.2.55 29 | einops==0.8.0 30 | exceptiongroup==1.2.2 31 | fancy-einsum==0.0.3 32 | filelock==3.15.4 33 | flash-attn==2.6.3 34 | fonttools==4.53.1 35 | frozenlist==1.4.1 36 | fsspec==2024.5.0 37 | gitdb==4.0.11 38 | GitPython==3.1.43 39 | gprof2dot==2024.6.6 40 | graze==0.1.24 41 | h11==0.14.0 42 | hjson==3.1.0 43 | httpcore==1.0.5 44 | httpx==0.27.0 45 | huggingface-hub==0.24.5 46 | hydra-core==1.3.2 47 | i2==0.1.18 48 | idna==3.7 49 | importlib_resources==6.4.0 50 | iniconfig==2.0.0 51 | jaxtyping==0.2.33 52 | Jinja2==3.1.4 53 | joblib==1.4.2 54 | kiwisolver==1.4.5 55 | lightning==2.3.3 56 | lightning-utilities==0.11.6 57 | lxml==4.9.4 58 | markdown-it-py==3.0.0 59 | MarkupSafe==2.1.5 60 | matplotlib==3.9.1 61 | matplotlib-inline==0.1.7 62 | mdurl==0.1.2 63 | mpmath==1.3.0 64 | multidict==6.0.5 65 | multiprocess==0.70.16 66 | networkx==3.3 67 | ninja==1.11.1.1 68 | nltk==3.8.1 69 | numpy==1.26.4 70 | nvidia-cublas-cu12==12.1.3.1 71 | nvidia-cuda-cupti-cu12==12.1.105 72 | nvidia-cuda-nvrtc-cu12==12.1.105 73 | nvidia-cuda-runtime-cu12==12.1.105 74 | nvidia-cudnn-cu12==9.1.0.70 75 | nvidia-cufft-cu12==11.0.2.54 76 | nvidia-curand-cu12==10.3.2.106 77 | nvidia-cusolver-cu12==11.4.5.107 78 | nvidia-cusparse-cu12==12.1.0.106 79 | nvidia-ml-py==12.555.43 80 | nvidia-nccl-cu12==2.20.5 81 | nvidia-nvjitlink-cu12==12.6.20 82 | nvidia-nvtx-cu12==12.1.105 83 | omegaconf==2.3.0 84 | orjson==3.10.6 85 | packaging==24.1 86 | pandas==2.2.2 87 | patsy==0.5.6 88 | pbr==6.0.0 89 | pillow==10.4.0 90 | platformdirs==4.2.2 91 | plotly==5.23.0 92 | plotly-express==0.4.1 93 | pluggy==1.5.0 94 | protobuf==5.27.3 95 | psutil==6.0.0 96 | py-cpuinfo==9.0.0 97 | py2store==0.1.20 98 | pyarrow==17.0.0 99 | pyarrow-hotfix==0.6 100 | pycryptodomex==3.20.0 101 | pydantic==2.8.2 102 | pydantic_core==2.20.1 103 | Pygments==2.18.0 104 | pyparsing==3.1.2 105 | pytest==8.3.2 106 | pytest-profiling==1.7.0 107 | python-dateutil==2.9.0.post0 108 | python-dotenv==1.0.1 109 | pytorch-lightning==2.3.3 110 | pytz==2024.1 111 | PyYAML==6.0.1 112 | pyzmq==26.0.0 113 | regex==2024.7.24 114 | requests==2.32.3 115 | rich==13.7.1 116 | sae-lens==3.13.1 117 | safetensors==0.4.3 118 | scikit-learn==1.5.1 119 | scipy==1.14.0 120 | sentencepiece==0.2.0 121 | sentry-sdk==2.12.0 122 | setproctitle==1.3.3 123 | shellingham==1.5.4 124 | shtab==1.7.1 125 | six==1.16.0 126 | smmap==5.0.1 127 | sniffio==1.3.1 128 | statsmodels==0.14.2 129 | stevedore==5.2.0 130 | sympy==1.13.1 131 | tenacity==9.0.0 132 | threadpoolctl==3.5.0 133 | tiktoken==0.6.0 134 | tokenizers==0.19.1 135 | tomli==2.0.1 136 | torch==2.4.0 137 | torchmetrics==1.4.1 138 | tqdm==4.66.5 139 | traitlets==5.14.3 140 | transformer-lens==2.3.0 141 | transformers==4.43.3 142 | triton==3.0.0 143 | trl==0.10.1 144 | typeguard==2.13.3 145 | typer==0.12.3 146 | typing_extensions==4.12.2 147 | tyro==0.8.10 148 | tzdata==2024.1 149 | urllib3==2.2.2 150 | uvloop==0.19.0 151 | virtualenv==20.26.3 152 | virtualenv-clone==0.5.7 153 | virtualenvwrapper==6.1.0 154 | wandb==0.17.5 155 | xxhash==3.4.1 156 | yarl==1.9.4 157 | zstandard==0.22.0 158 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | export XDG_CACHE_HOME=/mnt/weka/hw_workspace/qy_workspace/emo-lightning/FeatureAlignment/.cache 2 | CUDA_VISIBLE_DEVICES=0,1 python train.py -------------------------------------------------------------------------------- /sample.py: -------------------------------------------------------------------------------- 1 | import json 2 | import torch 3 | import argparse 4 | import math 5 | from tqdm import tqdm 6 | from transformers import AutoModelForCausalLM, AutoTokenizer 7 | from datasets import load_dataset 8 | from torch.utils.data import DataLoader 9 | 10 | # Argument parser 11 | def parse_args(): 12 | parser = argparse.ArgumentParser(description="Generate AlpacaEval responses with a pretrained model") 13 | parser.add_argument('--model_name_or_path', type=str, default='google/gemma-2-2b', help='Path to the model or model name from Hugging Face hub') 14 | parser.add_argument('--checkpoint_path', type=str, required=True, help='Path to the checkpoint file') 15 | parser.add_argument('--dataset_name', type=str, default='tatsu-lab/alpaca_eval', help='Name of the dataset from Hugging Face') 16 | parser.add_argument('--split', type=str, default='eval', help='Dataset split to use (e.g., train, eval)') 17 | parser.add_argument('--batch_size', type=int, default=8, help='Batch size for generation') 18 | parser.add_argument('--max_length', type=int, default=100, help='Maximum length of the generated output') 19 | parser.add_argument('--output_file', type=str, default='alpaca_eval_results.json', help='File to save the generated results in JSON format') 20 | parser.add_argument('--max_batches', type=int, default=100, help='Maximum number of batches to process') 21 | parser.add_argument('--temperature', type=float, default=1, help='Maximum number of batches to process') 22 | parser.add_argument('--entropy', type=bool, default=False, help='Maximum number of batches to process') 23 | parser.add_argument('--fm', type=bool, default=False, help='Maximum number of batches to process') 24 | return parser.parse_args() 25 | 26 | # Batch generate responses 27 | def generate_responses(model, tokenizer, instructions, template, max_length, temperature): 28 | prompts = [template.format(instruction) for instruction in instructions] 29 | inputs = tokenizer(prompts, return_tensors='pt', padding=True, truncation=True).input_ids.to('cuda') 30 | outputs = model.generate(inputs, max_new_tokens=max_length, pad_token_id=tokenizer.eos_token_id, temperature=temperature, do_sample=True) 31 | responses = tokenizer.batch_decode(outputs, skip_special_tokens=True) 32 | return responses 33 | 34 | def get_entropy(model, tokenizer, instructions, template, max_length, temperature): 35 | prompts = instructions 36 | inputs = tokenizer(prompts, return_tensors='pt', padding=True, truncation=True).input_ids.to('cuda') 37 | logits = model(inputs, return_dict=True).logits.detach() 38 | probs = torch.nn.functional.softmax(logits, dim=-1) 39 | log_probs = torch.nn.functional.log_softmax(logits, dim=-1) 40 | entropy = -torch.sum(probs * log_probs, dim=-1).mean(dim=-1) 41 | del logits, probs, log_probs 42 | return entropy 43 | 44 | def get_fm(model, tokenizer, instructions, template, max_length, temperature, sae_encoder): 45 | # prompts = [template.format(instruction) for instruction in instructions] 46 | inputs = tokenizer(instructions, return_tensors='pt', padding=True, truncation=True).input_ids.to('cuda') 47 | hidden_states = model(inputs, return_dict=True, output_hidden_states=True).hidden_states 48 | fm = sae_encoder.encode(hidden_states[-1]) 49 | return fm 50 | 51 | @torch.no_grad 52 | def main(): 53 | # Parse the arguments 54 | args = parse_args() 55 | 56 | 57 | # Load model and tokenizer 58 | model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path) 59 | sft_model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path) 60 | tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path) 61 | 62 | # Load checkpoint 63 | model.load_state_dict(torch.load(args.checkpoint_path)['state'], strict=False) 64 | sft_model.load_state_dict(torch.load("cache/sft-gemma-2-2b/LATEST/policy.pt")['state'], strict=False) 65 | model = model.to('cuda') 66 | sft_model = sft_model.to('cuda') 67 | 68 | if args.fm: 69 | # load sae 70 | from feature_map import get_feature_map 71 | sae_encoder = get_feature_map( 72 | model_name_or_path="google/gemma-2-2b-it", 73 | sae_encoder_name_or_path="google/gemma-scope-2b-pt-res", 74 | sae_layer_id=25, 75 | temperature=1.0, 76 | visualize=True, 77 | cache_dir=".cache", 78 | release=True, 79 | ) 80 | sae_encoder = sae_encoder.to('cuda') 81 | sae_encoder.eval().half() 82 | 83 | # Enable half precision (fp16) for faster inference 84 | model.half() 85 | sft_model.half() 86 | 87 | # Load dataset from Hugging Face hub 88 | if "jsonl" in args.dataset_name: 89 | dataset = load_dataset('json', data_files=args.dataset_name, split=args.split) 90 | else: dataset = load_dataset(args.dataset_name, split=args.split) 91 | 92 | # Define input template 93 | template = "<|user|>{}<|assistant|>" 94 | 95 | # Set up the DataLoader 96 | dataloader = DataLoader(dataset, batch_size=args.batch_size) 97 | 98 | # Generate results 99 | results = [] 100 | entropys = 0 101 | fm = 0 102 | for i, batch in tqdm(enumerate(dataloader), total=args.max_batches // args.batch_size + 1): 103 | if i >= (args.max_batches // args.batch_size + 1): 104 | break 105 | if "arena" in args.dataset_name: 106 | instructions = batch['turns'][0]['content'] 107 | elif "ultrafeedback" in args.dataset_name: 108 | instructions = batch['rejected'][0]['content'] 109 | responeses = batch['rejected'][1]['content'] 110 | instructions_all = [] 111 | for instruction, response in zip(instructions, responeses): 112 | instructions_all.append(template.format(instruction) + response) 113 | instructions = instructions_all 114 | 115 | if args.entropy: 116 | # compute the logit entropy of the model 117 | entropy = get_entropy(model, tokenizer, instructions, template, args.max_length, args.temperature) 118 | entropys += entropy 119 | elif args.fm: 120 | fm_one = get_fm(model, tokenizer, instructions, template, args.max_length, args.temperature, sae_encoder) 121 | fm_sft = get_fm(sft_model, tokenizer, instructions, template, args.max_length, args.temperature, sae_encoder) 122 | 123 | # calculate mse loss 124 | fm += torch.nn.functional.mse_loss(fm_one, fm_sft) 125 | else: 126 | responses = generate_responses(model, tokenizer, instructions, template, args.max_length, args.temperature) 127 | for instruction, response in zip(instructions, responses): 128 | result = { 129 | "instruction": instruction, 130 | "output": response, 131 | "generator": "gemma", 132 | "dataset": "helpful_base", # This can be customized or dynamic based on dataset 133 | "datasplit": args.split 134 | } 135 | results.append(result) 136 | 137 | if args.entropy: 138 | print(f"Average entropy: {entropys / (args.max_batches // args.batch_size + 1)}") 139 | elif args.fm: 140 | print(fm) 141 | # # draw the feature map 142 | # import matplotlib.pyplot as plt 143 | # import numpy as np 144 | # # fm = fm / (args.max_batches // args.batch_size + 1) 145 | # fm = fm.mean(dim=0) 146 | 147 | # # flatten fm as a 2D array 148 | # N = math.ceil(math.sqrt(fm.shape[0])) 149 | # fm = fm[:N*N].reshape(N, N) 150 | 151 | # fm = fm.cpu().numpy() 152 | # fm = np.squeeze(fm) 153 | # plt.imshow(fm, cmap='Blues', interpolation='nearest') 154 | # plt.savefig("fm_dpo.pdf") 155 | 156 | 157 | else: 158 | # Save results to JSON file 159 | with open(args.output_file, 'w') as f: 160 | json.dump(results, f, indent=4) 161 | 162 | print(f"Results saved to {args.output_file}") 163 | 164 | if __name__ == "__main__": 165 | main() -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | """ 2 | Main script for training. 3 | 4 | Sample use is: 5 | 6 | python train.py loss=ppo model=llama30b datasets=[shp,hh,oasst] exp_name=archangel_sft+ppo_llama30b mode=train \ 7 | ++cache_dir=/data/models/archangel ++model.load_from=archangel_sft_llama30b/LATEST/policy.pt 8 | 9 | where 10 | - loss should have a file under config/loss that specifies the trainer in trainers.py and dataloader in dataloader.py 11 | - model should have a file under config/model 12 | - datasets is a list of datasets, each of which has a get_{name} function in dataloader.py 13 | - exp_name is the experiment name (on WANDB); model will be saved to the cache_dir/exp_name 14 | - model.load_from should be used for aligning a model that has already been finetuned 15 | 16 | Remember to allocate enough RAM before running this (you need aroundd 800 GB for Llama-13B). 17 | """ 18 | import hydra 19 | from omegaconf import DictConfig 20 | from lightning import Trainer, seed_everything 21 | from lightning.pytorch.utilities import rank_zero_info 22 | from omegaconf import DictConfig, OmegaConf 23 | from feature_alignment.utils.util import instantiate 24 | 25 | 26 | def configure_date(config: DictConfig, tokenizer): 27 | data_iterator_kwargs = dict( 28 | max_length=config.model.max_length, 29 | max_prompt_length=config.model.max_prompt_length, 30 | human_prefix=config.data.human_prefix, 31 | human_suffix=config.data.human_suffix, 32 | assistant_prefix=config.data.assistant_prefix, 33 | assistant_suffix=config.data.assistant_suffix, 34 | seed=config.seed, 35 | frac_unique_desirable=config.data.frac_unique_desirable, 36 | frac_unique_undesirable=config.data.frac_unique_undesirable, 37 | # control tokens taken from Korbak et al.'s (2023) "Pretraining Models with Human Feedback" 38 | # SFTDataLoader will use them for sampling; ConditionalSFTDataLoader for training 39 | chosen_control_token=(config.loss.chosen_control_token if config.loss.name == "csft" else None), 40 | rejected_control_token=(config.loss.rejected_control_token if config.loss.name == "csft" else None), 41 | ) 42 | data_loader_class = instantiate(config.loss.dataloader, instantiate_module=False) 43 | train_iterator = data_loader_class( 44 | config.datasets, 45 | tokenizer, 46 | split='train', 47 | batch_size=config.train_bs, 48 | n_epochs=1e7 if config.n_examples is None else config.n_epochs, 49 | n_examples=config.n_examples, 50 | **data_iterator_kwargs 51 | ) 52 | eval_iterator = data_loader_class( 53 | config.datasets, 54 | tokenizer, 55 | split='test', 56 | batch_size=config.eval_bs, 57 | n_examples=config.n_eval_examples, 58 | n_epochs=(1 if config.n_eval_examples is None else None), 59 | **data_iterator_kwargs 60 | ) 61 | return train_iterator, eval_iterator 62 | 63 | 64 | @hydra.main(config_path="config", config_name="config") 65 | def main(config: DictConfig): 66 | """Main entry point for training. Validates config, creates/initializes model(s), and kicks off worker process(es).""" 67 | 68 | # ----------- check missing key in config ----------- 69 | missing_keys = OmegaConf.missing_keys(config) 70 | if missing_keys: 71 | raise ValueError(f"Got missing keys in config:\n{missing_keys}") 72 | 73 | # ----------------- seed everything and login ----------------- 74 | seed_everything(config.seed) 75 | from huggingface_hub import login 76 | login(token=config.hf_token) 77 | 78 | # ----------------- load callbacks ------------------ 79 | rank_zero_info(f"Loading callbacks from {config.callbacks}") 80 | callbacks = [instantiate(cb) for cb in config.callbacks] 81 | 82 | # # ------------------- load logger ------------------- 83 | if config.debug == False: 84 | if hasattr(config.logger, "neptune_api_token") and config.logger.neptune_api_token is not None: 85 | from lightning.pytorch.loggers import NeptuneLogger 86 | 87 | logger = NeptuneLogger( 88 | api_key=config.logger.neptune_api_token, 89 | project=config.logger.neptune_project, 90 | ) 91 | else: 92 | from lightning.pytorch.loggers import WandbLogger 93 | 94 | logger = WandbLogger( 95 | project=config.logger.wandb.project, 96 | name=config.exp_name, 97 | ) 98 | logger.log_hyperparams(config) 99 | else: 100 | logger = None 101 | 102 | # # ----------------- load trainer ------------------- 103 | rank_zero_info(f"Loading trainer from {config.trainer}") 104 | if "FSDP" in config.trainer.strategy: 105 | from lightning.pytorch.strategies import FSDPStrategy 106 | strategy = FSDPStrategy( 107 | sharding_strategy=config.trainer.fsdp_sharding_strategy, 108 | state_dict_type=config.trainer.fsdp_state_dict_type, 109 | ) 110 | config.trainer.strategy = strategy 111 | trainer = Trainer( 112 | **config.trainer, 113 | callbacks=callbacks, 114 | logger=logger, 115 | ) 116 | 117 | # ----------------- load model --------------------- 118 | module = instantiate(config.loss.model, instantiate_module=False) 119 | rank_zero_info(f"Loading model from {config.loss.model.module_name}.{config.loss.model.class_name}") 120 | if config.resume_ckpt is not None: 121 | model = module.load_from_checkpoint(config.resume_ckpt) 122 | else: 123 | model = module(config=config) 124 | 125 | # ----------------- load tokenizer ----------------- 126 | rank_zero_info(f'Loading tokenizer {config.model.hf_tokenizer_name_or_path}') 127 | from transformers import AutoTokenizer 128 | tokenizer = AutoTokenizer.from_pretrained(config.model.hf_tokenizer_name_or_path) 129 | if tokenizer.pad_token_id is None: 130 | tokenizer.pad_token_id = tokenizer.eos_token_id 131 | 132 | # ----------------- load data ----------------------- 133 | rank_zero_info("Loading data") 134 | train_dataloader, eval_dataloader = configure_date(config, tokenizer) 135 | 136 | # ----------------- train model --------------------- 137 | trainer.fit( 138 | model, 139 | train_dataloader, 140 | ) 141 | 142 | 143 | if __name__ == "__main__": 144 | main() 145 | --------------------------------------------------------------------------------