feat(examples): release an example implementation of T-V reward model.

.flake8

@@ -9,11 +9,10 @@ ignore =
     # W503: line break before binary operator
     # W504: line break after binary operator
     # format by black
-    E203,E241,E704,W503,W504,
+    E203,E241,E704,W503,W504,E501,W505,
     # E501: line too long
     # W505: doc line too long
     # too long docstring due to long example blocks
-    E501,W505,
 per-file-ignores =
     # F401: module imported but unused
     # intentionally unused imports

.github/PULL_REQUEST_TEMPLATE.md

@@ -24,7 +24,6 @@ What types of changes does your code introduce? Put an `x` in all the boxes that
 Go over all the following points, and put an `x` in all the boxes that apply.
 If you are unsure about any of these, don't hesitate to ask. We are here to help!
 
-<!-- - [ ] I have read the [CONTRIBUTION](https://safe-sora.readthedocs.io/en/latest/developer/contributing.html) guide. (**required**) -->
 - [ ] My change requires a change to the documentation.
 - [ ] I have updated the tests accordingly. (*required for a bug fix or a new feature*)
 - [ ] I have updated the documentation accordingly.

.github/workflows/lint.yml

@@ -44,22 +44,6 @@ jobs:
         run: |
           make pre-commit
 
-      - name: ruff
-        run: |
-          make ruff
-
-      - name: flake8
-        run: |
-          make flake8
-
-      - name: pylint
-        run: |
-          make pylint
-
-      - name: isort and black
-        run: |
-          make py-format
-
       - name: addlicense
         run: |
           make addlicense

.gitignore

@@ -1,9 +1,10 @@
 ##### Project Specification #####
-dataset/
 outputs/
 wandb/
 test/
 data/
+checkpoints/
+cache_dir
 
 ##### Python.gitignore #####
 # Byte-compiled / optimized / DLL files

.pre-commit-config.yaml

@@ -33,6 +33,11 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
+        exclude: |
+          (?x)(
+            ^safe_sora/models/multimodal_encoder/|
+            ^safe_sora/models/multimodal_projector/
+          )
   - repo: https://github.com/PyCQA/isort
     rev: 5.13.2
     hooks:
@@ -68,6 +73,8 @@ repos:
   - repo: local
     hooks:
       - id: pylint
+        args:
+          - --disable=R0801
         name: pylint
         entry: pylint
         language: system
@@ -78,5 +85,8 @@ repos:
             ^examples/|
             ^tests/|
             ^setup.py$|
+            ^safe_sora/models/multimodal_encoder/|
+            ^safe_sora/models/multimodal_projector/|
+            ^safe_sora/models/video_llava.py|
             ^docs/source/conf.py$
           )

Makefile

@@ -3,7 +3,7 @@ PROJECT_NAME   = safe-sora
 COPYRIGHT      = "PKU-Alignment Team. All Rights Reserved."
 PROJECT_PATH   = safe_sora
 SHELL          = /bin/bash
-SOURCE_FOLDERS = $(PROJECT_PATH) examples tests docs
+SOURCE_FOLDERS = $(PROJECT_PATH) examples docs
 PYTHON_FILES   = $(shell find $(SOURCE_FOLDERS) -type f -name "*.py" -o -name "*.pyi")
 COMMIT_HASH    = $(shell git log -1 --format=%h)
 PATH           := $(HOME)/go/bin:$(PATH)
@@ -130,7 +130,7 @@ pre-commit: pre-commit-install
 # Documentation
 
 addlicense: addlicense-install
-	addlicense -c $(COPYRIGHT) -ignore tests/coverage.xml -l apache -y 2022-$(shell date +"%Y") -check $(SOURCE_FOLDERS)
+	addlicense -c $(COPYRIGHT) -ignore **/multimodal_encoder/** -ignore **/multimodal_projector/** -l apache -y 2022-$(shell date +"%Y") -check $(SOURCE_FOLDERS)
 
 docstyle: docs-install
 	make -C docs clean

conda-recipe.yaml

@@ -0,0 +1,56 @@
+# Copyright 2024 PKU-Alignment Team. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Create virtual environment with command:
+#
+#   $ CONDA_OVERRIDE_CUDA=11.8 conda env create --file conda-recipe.yaml
+#
+
+name: safe-sora
+channels:
+  - huggingface
+  - pytorch
+  - nvidia/label/cuda-12.1.0
+  - defaults
+  - conda-forge
+dependencies:
+  - python = 3.11
+  - pip
+
+  - pytorch::pytorch >= 2.0
+  - pytorch::pytorch-mutex =*=*cuda*
+  - pytorch::torchvision
+  - transformers >= 4.42
+  - datasets
+  - tokenizers >= 0.19
+  - sentencepiece
+  - tensorboard
+  - wandb
+  - pip:
+      - accelerate
+      - deepspeed
+      - decord
+      - opencv-python
+
+  - nvidia/label/cuda-12.1.0::cuda-toolkit = 12.1
+
+  - matplotlib-base
+  - rich
+  - tqdm
+  - typing-extensions
+  - bitsandbytes
+  - av
+  - einops
+  - peft

examples/README.md

@@ -0,0 +1,63 @@
+<!-- markdownlint-disable html -->
+
+# Preference Model
+
+In this directory, we provide an example implementation of training a preference predictor reward model on our dataset.
+
+## Preference Modeling
+
+To modeling human preferences, it's common to use a preference predictor adhering to the Bradley-Terry Model. The preference data is symbolized as $y_w \succ y_{l} | x$ where $y_{w}$ denotes the more preferred video than $y_l$ corresponding to the prompt $x$.
+The log-likelihood loss used to train a parameterized predictor $R_\phi$ on dataset $\mathcal{D}$ is:
+
+$$\mathcal{L} (\phi; \mathcal{D}) = -\mathbb E_{{(x,y_w,y_l)\sim \mathcal{D}}} \left[\log \sigma (R_{\phi} (y_w,x) - R_{\phi} (y_l,x))\right]$$
+
+
+Leveraging a multi-modal model architecture modified on the [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) and training with preference data from [SafeSora Dataset](https://huggingface.co/datasets/PKU-Alignment/SafeSora), we have develop a T-V reward model.
+The language head of the vision-language model is replaced with a score regression head, which predicts the preference score of the video given the prompt.
+
+This model translates abstract human values into quantifiable and optimizable scalar metrics.
+Consequently, the reward model can partially replace human evaluators in assessing outputs from video generation models and act as a supervisory signal to enhance the performance of these models.
+
+## Alignment Evaluation of Different Models
+
+The SafeSora dataset includes annotations across multiple dimensions of human preference. We have developed several distinct models that focus on different aspects of human preference, such as helpfulness, harmlessness, and four specific sub-dimensions of helpfulness. Our models achieve an agreement ratio of 65.29% for predicting helpfulness preference and 72.41% for predicting harmlessness preference when compared with crowdworker assessments.
+
+Furthermore, we utilize these models to evaluate four open-source models on our [Evaluation Dataset](https://huggingface.co/datasets/PKU-Alignment/SafeSora-Eval). The win-rate relationships among these models, assessed across the two alignment dimensions, are depicted in the figure below.
+
+<div align="center">
+  <img src="../docs/images/win_rate.png" alt="win_rate" width="85%"/>
+</div>
+
+## Training
+
+First, you need to [download our dataset](../README.md#data-access) to local and prepare the training environment using:
+
+```bash
+conda env create -f conda-recipe.yaml  # mamba env create -f conda-recipe.yaml
+conda activate safe-sora
+```
+
+Then, you need to download the Video-LLaVA model and the MM-MLP adapter from the Hugging Face model hub. For example, you can download them use the following commands:
+
+```bash
+huggingface-cli download --resume-download LanguageBind/Video-LLaVA-7B --local-dir ./LanguageBind/Video-LLaVA-7B
+huggingface-cli download --resume-download LanguageBind/Video-LLaVA-Pretrain-7B --local-dir ./LanguageBind/Video-LLaVA-Pretrain-7B
+```
+
+Then, you can run the following script to train the reward model on the SafeSora dataset:
+
+```bash
+bash examples/scripts/finetune_reward_model.sh \
+    --model_name_or_path <your-model-name-or-checkpoint-path> \
+    --mm_mlp_adapter_path <your-mm_mlp_adapter_path> \
+    --dimension <the-target-dimension-to-train> \
+    --output_dir examples/outputs/reward-model
+```
+
+where `<your-model-name-or-checkpoint-path>` is the name of the Video-LLaVA model or the path to the checkpoint directory, `<your-mm_mlp_adapter_path>` is the path to the `mm_projector.bin` file, and `<the-target-dimension-to-train>` is the preference dimension that the reward model will predict.
+
+**NOTE:** The parameter 'dimension' specifies the preference dimension that the reward model will predict. The SafeSora dataset currently supports the following dimensions: `helpfulness`, `harmlessness`, `instruction_following`, `correctness`, `informativeness`, and `aesthetics`. For the detailed information of the different dimensions, please refer to our [paper](https://arxiv.org/abs/2406.14477).
+
+## Acknowledgements
+
+This implementation benefits from [DeepSpeed](https://github.com/microsoft/DeepSpeed), [Transformers](https://github.com/huggingface/transformers), [LLaVA](https://github.com/haotian-liu/LLaVA), and [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA). Thanks for their wonderful works and their efforts for democratizing the LLM research.