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Browse files- README.md +192 -2
- train.ipynb +0 -656
README.md
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short_description: Convert pseudocode to C++ using a Transformer model.
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---
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---
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title: Pseudo2Code
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emoji: 👀
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colorFrom: yellow
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colorTo: gray
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short_description: Convert pseudocode to C++ using a Transformer model.
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---
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# 🚀 Pseudo2Code – Transformer-based Pseudocode to C++ Converter
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[](LICENSE)
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[](https://www.python.org/)
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[](https://huggingface.co/spaces/asadsandhu/Pseudo2Code)
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[](https://github.com/asadsandhu/Pseudo2Code)
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> A fully custom Transformer-based Sequence-to-Sequence model built from scratch in PyTorch to convert human-written pseudocode into executable C++ code. Trained on the [SPoC dataset](https://arxiv.org/abs/2005.04326) from Stanford.
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---
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## 🖼️ Demo
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Try it live on **Hugging Face Spaces**:
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👉 https://huggingface.co/spaces/asadsandhu/Pseudo2Code
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---
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## 🧠 Model Architecture
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- Developed using the **Transformer** architecture from scratch in PyTorch
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- No pre-trained models (pure from-scratch implementation)
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- Token-level sequence generation using greedy decoding
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- Custom vocabulary construction for both pseudocode and C++ output
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```
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Input: Pseudocode lines (line-by-line)
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Model: Transformer (Encoder-Decoder)
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Output: C++ code line for each pseudocode line
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```
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---
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## 📊 Dataset
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We used the **SPoC dataset** from Stanford:
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- ✅ Clean pseudocode–C++ line pairs
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- ✅ Token-level annotations for syntax handling
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- ✅ Multiple test splits (generalization to problems/workers)
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- ✅ Custom preprocessing and vocabulary building implemented
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> 📎 Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
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---
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## 📁 Directory Structure
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```
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.
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├── app.py # Gradio web app for inference
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├── train.py # Transformer training code
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├── model.pth # Trained model weights
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├── spoc/ # Dataset directory
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│ └── train/
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│ ├── spoc-train.tsv
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│ └── split/spoc-train-eval.tsv
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├── assets/
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│ └── demo.png # App screenshot
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└── README.md # You're here
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````
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---
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## 🛠️ How to Run Locally
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### ⚙️ 1. Clone Repo & Install Requirements
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```bash
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git clone https://github.com/asadsandhu/Pseudo2Code.git
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cd Pseudo2Code
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pip install -r requirements.txt
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````
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Or manually install:
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```bash
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pip install torch gradio tqdm
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```
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### 🚀 2. Launch the App
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Make sure `model.pth` is present (or train using `train.py`):
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```bash
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python app.py
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```
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The app will open in your browser.
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---
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## 🧪 Training the Model
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You can retrain the model using the `train.py` script:
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```bash
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python train.py
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```
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By default, it downloads data from the public repo and trains for 10 epochs.
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Outputs a `model.pth` file with learned weights and vocab.
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---
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## 🔧 Key Hyperparameters
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| Parameter | Value |
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| -------------- | ----------- |
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| Model Type | Transformer |
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| Max Length | 128 |
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| Embedding Dim | 256 |
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| FFN Dim | 512 |
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| Heads | 4 |
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| Encoder Layers | 2 |
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| Decoder Layers | 2 |
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| Batch Size | 64 |
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| Epochs | 10 |
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| Optimizer | Adam |
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| Learning Rate | 1e-4 |
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---
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## 🧩 Example Input
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```text
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n , nn, ans = integers with ans =0
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Read n
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for i=2 to n-1 execute
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set nn to n
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while nn is not equal to 0, set ans to ans + nn%i, and also set nn= nn/i
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}
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set o to gcd(ans, n-2)
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print out ans/o "/" (n-2)/o
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```
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### ⏩ Output C++
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```cpp
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int main() {
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int n , nn , ans = 0 ;
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cin > > n ;
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for ( int i = 2 ; i < = n - 1 ; i + + ) {
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nn = n ;
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while ( nn = = 0 ) ans + = nn % i , nn / = i ;
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}
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o = gcd ( ans , n - 2 ) ;
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cout < < ans / 2 / o ( n - 2 ) / o < < endl ;
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return 0;
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}
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```
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---
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## 📦 Deployment
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This app is deployed live on:
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* **Hugging Face Spaces**: [Pseudo2Code](https://huggingface.co/spaces/asadsandhu/Pseudo2Code)
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* **GitHub**: [github.com/asadsandhu/Pseudo2Code](https://github.com/asadsandhu/Pseudo2Code)
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---
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## 🙌 Acknowledgements
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* 📘 **SPoC Dataset** by Stanford University
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Kulal, S., Pasupat, P., & Liang, P. (2020). [SPoC: Search-based Pseudocode to Code](https://arxiv.org/abs/2005.04326)
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* 🧠 Transformer Paper: ["Attention is All You Need"](https://arxiv.org/abs/1706.03762)
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---
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## 🧑💻 Author
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**Asad Ali**
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[GitHub: asadsandhu](https://github.com/asadsandhu)
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[Hugging Face: asadsandhu](https://huggingface.co/asadsandhu)
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[LinkedIn: asadxali](https://www.linkedin.com/in/asadxali)
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---
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## 📄 License
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This project is licensed under the MIT License.
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Feel free to use, modify, and share with credit.
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train.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"collapsed": true,
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"id": "12APLOKE15uD",
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"outputId": "fb61078b-a249-476a-af53-e43ca978c8c1"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
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"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
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"Requirement already satisfied: streamlit in /usr/local/lib/python3.11/dist-packages (1.42.2)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.17.0)\n",
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"Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.12.2)\n",
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"Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.5)\n",
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"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
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"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.27->streamlit) (2.3.0)\n",
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]
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}
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| 98 |
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],
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| 99 |
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"source": [
|
| 100 |
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"!pip install torch tqdm streamlit"
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| 101 |
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]
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| 102 |
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},
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| 103 |
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{
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| 104 |
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"cell_type": "code",
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| 105 |
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"source": [
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| 106 |
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"######################################\n",
|
| 107 |
-
"# Pseudocode2Cpp.py\n",
|
| 108 |
-
"######################################\n",
|
| 109 |
-
"import os\n",
|
| 110 |
-
"import streamlit as st\n",
|
| 111 |
-
"import torch\n",
|
| 112 |
-
"import torch.nn as nn\n",
|
| 113 |
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"import torch.optim as optim\n",
|
| 114 |
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"import math\n",
|
| 115 |
-
"import re\n",
|
| 116 |
-
"from tqdm import tqdm\n",
|
| 117 |
-
"from typing import List, Tuple\n",
|
| 118 |
-
"import random\n",
|
| 119 |
-
"import requests\n",
|
| 120 |
-
"from torch.utils.data import DataLoader, TensorDataset"
|
| 121 |
-
],
|
| 122 |
-
"metadata": {
|
| 123 |
-
"id": "tEYW8hGR19sm"
|
| 124 |
-
},
|
| 125 |
-
"execution_count": null,
|
| 126 |
-
"outputs": []
|
| 127 |
-
},
|
| 128 |
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{
|
| 129 |
-
"cell_type": "code",
|
| 130 |
-
"source": [
|
| 131 |
-
"# ----------------------------\n",
|
| 132 |
-
"# 1. Hyperparameters\n",
|
| 133 |
-
"# ----------------------------\n",
|
| 134 |
-
"DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
| 135 |
-
"MAX_LEN = 128 # maximum sequence length\n",
|
| 136 |
-
"EMBED_DIM = 256 # embedding dimension\n",
|
| 137 |
-
"FF_DIM = 512 # feedforward dimension in Transformer\n",
|
| 138 |
-
"NHEAD = 4 # number of heads in multihead attention\n",
|
| 139 |
-
"NUM_ENCODER_LAYERS = 2\n",
|
| 140 |
-
"NUM_DECODER_LAYERS = 2\n",
|
| 141 |
-
"BATCH_SIZE = 64\n",
|
| 142 |
-
"EPOCHS = 10 # Increase for real training\n",
|
| 143 |
-
"LEARNING_RATE = 1e-4\n",
|
| 144 |
-
"\n",
|
| 145 |
-
"# Special tokens\n",
|
| 146 |
-
"PAD_TOKEN = \"<pad>\"\n",
|
| 147 |
-
"SOS_TOKEN = \"<sos>\"\n",
|
| 148 |
-
"EOS_TOKEN = \"<eos>\"\n",
|
| 149 |
-
"UNK_TOKEN = \"<unk>\""
|
| 150 |
-
],
|
| 151 |
-
"metadata": {
|
| 152 |
-
"id": "HelkrJ-01-2B"
|
| 153 |
-
},
|
| 154 |
-
"execution_count": null,
|
| 155 |
-
"outputs": []
|
| 156 |
-
},
|
| 157 |
-
{
|
| 158 |
-
"cell_type": "code",
|
| 159 |
-
"source": [
|
| 160 |
-
"# ----------------------------\n",
|
| 161 |
-
"# 2. Data Loading & Preprocessing\n",
|
| 162 |
-
"# ----------------------------\n",
|
| 163 |
-
"\n",
|
| 164 |
-
"def load_spoc_data(file_path: str):\n",
|
| 165 |
-
" \"\"\"\n",
|
| 166 |
-
" Loads (pseudo_code, cpp_code) pairs from a TSV file or raw GitHub link.\n",
|
| 167 |
-
" Each line is assumed to have: pseudocode <tab> c++ code.\n",
|
| 168 |
-
" \"\"\"\n",
|
| 169 |
-
" pairs = []\n",
|
| 170 |
-
"\n",
|
| 171 |
-
" # If file_path is a URL, fetch it with requests\n",
|
| 172 |
-
" if file_path.startswith(\"http\"):\n",
|
| 173 |
-
" response = requests.get(file_path)\n",
|
| 174 |
-
" response.raise_for_status()\n",
|
| 175 |
-
" lines = response.text.strip().split(\"\\n\")\n",
|
| 176 |
-
" else:\n",
|
| 177 |
-
" # Otherwise, assume it's a local file path\n",
|
| 178 |
-
" with open(file_path, 'r', encoding='utf-8') as f:\n",
|
| 179 |
-
" lines = f.readlines()\n",
|
| 180 |
-
"\n",
|
| 181 |
-
" for line in lines:\n",
|
| 182 |
-
" line = line.strip()\n",
|
| 183 |
-
" if not line:\n",
|
| 184 |
-
" continue\n",
|
| 185 |
-
" cols = line.split('\\t')\n",
|
| 186 |
-
" if len(cols) >= 2:\n",
|
| 187 |
-
" pseudo = cols[0].strip()\n",
|
| 188 |
-
" cpp = cols[1].strip()\n",
|
| 189 |
-
" pairs.append((pseudo, cpp))\n",
|
| 190 |
-
"\n",
|
| 191 |
-
" return pairs\n",
|
| 192 |
-
"\n",
|
| 193 |
-
"def create_dataloader(pairs, src_stoi, tgt_stoi, batch_size):\n",
|
| 194 |
-
" src_batches = []\n",
|
| 195 |
-
" tgt_batches = []\n",
|
| 196 |
-
" for pseudo, cpp in pairs:\n",
|
| 197 |
-
" src_ids = pad_sequence(numericalize(pseudo, src_stoi), MAX_LEN, src_stoi[PAD_TOKEN])\n",
|
| 198 |
-
" tgt_ids = pad_sequence(numericalize(cpp, tgt_stoi), MAX_LEN, tgt_stoi[PAD_TOKEN])\n",
|
| 199 |
-
" src_batches.append(src_ids)\n",
|
| 200 |
-
" tgt_batches.append(tgt_ids)\n",
|
| 201 |
-
"\n",
|
| 202 |
-
" src_tensor = torch.tensor(src_batches, dtype=torch.long)\n",
|
| 203 |
-
" tgt_tensor = torch.tensor(tgt_batches, dtype=torch.long)\n",
|
| 204 |
-
" dataset = TensorDataset(src_tensor, tgt_tensor)\n",
|
| 205 |
-
" return DataLoader(dataset, batch_size=batch_size, shuffle=True, pin_memory=True)\n",
|
| 206 |
-
"\n",
|
| 207 |
-
"def tokenize_line(text: str) -> List[str]:\n",
|
| 208 |
-
" \"\"\"Enhanced tokenizer for pseudocode/C++ patterns\"\"\"\n",
|
| 209 |
-
" # Separate operators and punctuation\n",
|
| 210 |
-
" text = re.sub(r'([=+\\-*/%<>!&|^~])', r' \\1 ', text) # Operators\n",
|
| 211 |
-
" text = re.sub(r'(?<!:):(?!:)', r' : ', text) # Single colon\n",
|
| 212 |
-
" return re.findall(r'\\b\\w+\\b|[-+*/%=<>!&|^~]+|[:;{},()\\[\\]\\.]', text)\n",
|
| 213 |
-
"\n",
|
| 214 |
-
"def build_vocab(pairs: List[Tuple[str, str]]) -> Tuple[dict, dict, dict, dict]:\n",
|
| 215 |
-
" \"\"\"\n",
|
| 216 |
-
" Build source (pseudo) and target (cpp) vocabularies from training data.\n",
|
| 217 |
-
" Returns:\n",
|
| 218 |
-
" src_stoi, src_itos, tgt_stoi, tgt_itos\n",
|
| 219 |
-
" \"\"\"\n",
|
| 220 |
-
" src_words = set()\n",
|
| 221 |
-
" tgt_words = set()\n",
|
| 222 |
-
"\n",
|
| 223 |
-
" for (pseudo, cpp) in pairs:\n",
|
| 224 |
-
" for tok in tokenize_line(pseudo):\n",
|
| 225 |
-
" src_words.add(tok)\n",
|
| 226 |
-
" for tok in tokenize_line(cpp):\n",
|
| 227 |
-
" tgt_words.add(tok)\n",
|
| 228 |
-
"\n",
|
| 229 |
-
" # Add special tokens\n",
|
| 230 |
-
" src_vocab = [PAD_TOKEN, SOS_TOKEN, EOS_TOKEN, UNK_TOKEN] + sorted(list(src_words))\n",
|
| 231 |
-
" tgt_vocab = [PAD_TOKEN, SOS_TOKEN, EOS_TOKEN, UNK_TOKEN] + sorted(list(tgt_words))\n",
|
| 232 |
-
"\n",
|
| 233 |
-
" src_stoi = {w: i for i, w in enumerate(src_vocab)}\n",
|
| 234 |
-
" src_itos = {i: w for i, w in enumerate(src_vocab)}\n",
|
| 235 |
-
" tgt_stoi = {w: i for i, w in enumerate(tgt_vocab)}\n",
|
| 236 |
-
" tgt_itos = {i: w for i, w in enumerate(tgt_vocab)}\n",
|
| 237 |
-
"\n",
|
| 238 |
-
" return src_stoi, src_itos, tgt_stoi, tgt_itos\n",
|
| 239 |
-
"\n",
|
| 240 |
-
"def numericalize(text: str, stoi: dict) -> List[int]:\n",
|
| 241 |
-
" \"\"\"\n",
|
| 242 |
-
" Convert text string to a list of token IDs.\n",
|
| 243 |
-
" \"\"\"\n",
|
| 244 |
-
" tokens = tokenize_line(text)\n",
|
| 245 |
-
" ids = []\n",
|
| 246 |
-
" for t in tokens:\n",
|
| 247 |
-
" if t in stoi:\n",
|
| 248 |
-
" ids.append(stoi[t])\n",
|
| 249 |
-
" else:\n",
|
| 250 |
-
" ids.append(stoi[UNK_TOKEN])\n",
|
| 251 |
-
" return ids\n",
|
| 252 |
-
"\n",
|
| 253 |
-
"def pad_sequence(seq: List[int], max_len: int, pad_id: int) -> List[int]:\n",
|
| 254 |
-
" \"\"\"Proper padding with SOS/EOS handling\"\"\"\n",
|
| 255 |
-
" seq = seq[:max_len-2] # Leave space for SOS/EOS\n",
|
| 256 |
-
" seq = [src_stoi[SOS_TOKEN]] + seq + [src_stoi[EOS_TOKEN]] # Add control tokens\n",
|
| 257 |
-
" padding = [pad_id] * (max_len - len(seq))\n",
|
| 258 |
-
" return seq + padding\n",
|
| 259 |
-
"\n",
|
| 260 |
-
"def create_batches(pairs, src_stoi, tgt_stoi, batch_size):\n",
|
| 261 |
-
" \"\"\"\n",
|
| 262 |
-
" Yield batches of data (source_ids, target_ids).\n",
|
| 263 |
-
" \"\"\"\n",
|
| 264 |
-
" random.shuffle(pairs)\n",
|
| 265 |
-
" for i in range(0, len(pairs), batch_size):\n",
|
| 266 |
-
" batch_pairs = pairs[i:i+batch_size]\n",
|
| 267 |
-
" src_batch = []\n",
|
| 268 |
-
" tgt_batch = []\n",
|
| 269 |
-
" for pseudo, cpp in batch_pairs:\n",
|
| 270 |
-
" src_ids = numericalize(pseudo, src_stoi)\n",
|
| 271 |
-
" tgt_ids = numericalize(cpp, tgt_stoi)\n",
|
| 272 |
-
"\n",
|
| 273 |
-
" # Pad/truncate\n",
|
| 274 |
-
" src_ids = pad_sequence(src_ids, MAX_LEN, src_stoi[PAD_TOKEN])\n",
|
| 275 |
-
" tgt_ids = pad_sequence(tgt_ids, MAX_LEN, tgt_stoi[PAD_TOKEN])\n",
|
| 276 |
-
"\n",
|
| 277 |
-
" src_batch.append(src_ids)\n",
|
| 278 |
-
" tgt_batch.append(tgt_ids)\n",
|
| 279 |
-
"\n",
|
| 280 |
-
" src_batch = torch.tensor(src_batch, dtype=torch.long, device=DEVICE)\n",
|
| 281 |
-
" tgt_batch = torch.tensor(tgt_batch, dtype=torch.long, device=DEVICE)\n",
|
| 282 |
-
" yield src_batch, tgt_batch"
|
| 283 |
-
],
|
| 284 |
-
"metadata": {
|
| 285 |
-
"id": "2lFlkj-t2AGg"
|
| 286 |
-
},
|
| 287 |
-
"execution_count": null,
|
| 288 |
-
"outputs": []
|
| 289 |
-
},
|
| 290 |
-
{
|
| 291 |
-
"cell_type": "code",
|
| 292 |
-
"source": [
|
| 293 |
-
"# ----------------------------\n",
|
| 294 |
-
"# 3. Transformer Model Implementation (from scratch)\n",
|
| 295 |
-
"# ----------------------------\n",
|
| 296 |
-
"\n",
|
| 297 |
-
"class PositionalEncoding(nn.Module):\n",
|
| 298 |
-
" def __init__(self, d_model, max_len=5000):\n",
|
| 299 |
-
" super(PositionalEncoding, self).__init__()\n",
|
| 300 |
-
" pe = torch.zeros(max_len, d_model)\n",
|
| 301 |
-
" position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
|
| 302 |
-
" div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
|
| 303 |
-
" pe[:, 0::2] = torch.sin(position * div_term)\n",
|
| 304 |
-
" pe[:, 1::2] = torch.cos(position * div_term)\n",
|
| 305 |
-
" pe = pe.unsqueeze(0) # shape (1, max_len, d_model)\n",
|
| 306 |
-
" self.register_buffer('pe', pe)\n",
|
| 307 |
-
"\n",
|
| 308 |
-
" def forward(self, x):\n",
|
| 309 |
-
" # x shape: (batch_size, seq_len, d_model)\n",
|
| 310 |
-
" seq_len = x.size(1)\n",
|
| 311 |
-
" x = x + self.pe[:, :seq_len, :]\n",
|
| 312 |
-
" return x\n",
|
| 313 |
-
"\n",
|
| 314 |
-
"class MultiHeadAttention(nn.Module):\n",
|
| 315 |
-
" def __init__(self, d_model, n_heads):\n",
|
| 316 |
-
" super(MultiHeadAttention, self).__init__()\n",
|
| 317 |
-
" assert d_model % n_heads == 0\n",
|
| 318 |
-
" self.d_model = d_model\n",
|
| 319 |
-
" self.n_heads = n_heads\n",
|
| 320 |
-
" self.head_dim = d_model // n_heads\n",
|
| 321 |
-
"\n",
|
| 322 |
-
" self.query_linear = nn.Linear(d_model, d_model)\n",
|
| 323 |
-
" self.key_linear = nn.Linear(d_model, d_model)\n",
|
| 324 |
-
" self.value_linear = nn.Linear(d_model, d_model)\n",
|
| 325 |
-
" self.out_linear = nn.Linear(d_model, d_model)\n",
|
| 326 |
-
"\n",
|
| 327 |
-
" def forward(self, query, key, value, mask=None):\n",
|
| 328 |
-
" # query/key/value shape: (batch_size, seq_len, d_model)\n",
|
| 329 |
-
" B, Q_len, _ = query.size()\n",
|
| 330 |
-
" B, K_len, _ = key.size()\n",
|
| 331 |
-
" B, V_len, _ = value.size()\n",
|
| 332 |
-
"\n",
|
| 333 |
-
" # Linear projections\n",
|
| 334 |
-
" Q = self.query_linear(query) # (B, Q_len, d_model)\n",
|
| 335 |
-
" K = self.key_linear(key) # (B, K_len, d_model)\n",
|
| 336 |
-
" V = self.value_linear(value) # (B, V_len, d_model)\n",
|
| 337 |
-
"\n",
|
| 338 |
-
" # Reshape for multi-head\n",
|
| 339 |
-
" Q = Q.view(B, Q_len, self.n_heads, self.head_dim).transpose(1,2) # (B, n_heads, Q_len, head_dim)\n",
|
| 340 |
-
" K = K.view(B, K_len, self.n_heads, self.head_dim).transpose(1,2) # (B, n_heads, K_len, head_dim)\n",
|
| 341 |
-
" V = V.view(B, V_len, self.n_heads, self.head_dim).transpose(1,2) # (B, n_heads, V_len, head_dim)\n",
|
| 342 |
-
"\n",
|
| 343 |
-
" # Scaled dot-product attention\n",
|
| 344 |
-
" scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim) # (B, n_heads, Q_len, K_len)\n",
|
| 345 |
-
" if mask is not None:\n",
|
| 346 |
-
" scores = scores.masked_fill(mask == 0, float('-inf'))\n",
|
| 347 |
-
" attn = torch.softmax(scores, dim=-1) # (B, n_heads, Q_len, K_len)\n",
|
| 348 |
-
"\n",
|
| 349 |
-
" context = torch.matmul(attn, V) # (B, n_heads, Q_len, head_dim)\n",
|
| 350 |
-
" context = context.transpose(1,2).contiguous().view(B, Q_len, self.d_model)\n",
|
| 351 |
-
" out = self.out_linear(context)\n",
|
| 352 |
-
" return out\n",
|
| 353 |
-
"\n",
|
| 354 |
-
"class FeedForward(nn.Module):\n",
|
| 355 |
-
" def __init__(self, d_model, dim_feedforward):\n",
|
| 356 |
-
" super(FeedForward, self).__init__()\n",
|
| 357 |
-
" self.fc1 = nn.Linear(d_model, dim_feedforward)\n",
|
| 358 |
-
" self.fc2 = nn.Linear(dim_feedforward, d_model)\n",
|
| 359 |
-
" self.relu = nn.ReLU()\n",
|
| 360 |
-
"\n",
|
| 361 |
-
" def forward(self, x):\n",
|
| 362 |
-
" return self.fc2(self.relu(self.fc1(x)))\n",
|
| 363 |
-
"\n",
|
| 364 |
-
"class EncoderLayer(nn.Module):\n",
|
| 365 |
-
" def __init__(self, d_model, n_heads, dim_feedforward):\n",
|
| 366 |
-
" super(EncoderLayer, self).__init__()\n",
|
| 367 |
-
" self.self_attn = MultiHeadAttention(d_model, n_heads)\n",
|
| 368 |
-
" self.ff = FeedForward(d_model, dim_feedforward)\n",
|
| 369 |
-
" self.norm1 = nn.LayerNorm(d_model)\n",
|
| 370 |
-
" self.norm2 = nn.LayerNorm(d_model)\n",
|
| 371 |
-
" self.dropout = nn.Dropout(0.1)\n",
|
| 372 |
-
"\n",
|
| 373 |
-
" def forward(self, src, src_mask=None):\n",
|
| 374 |
-
" # Self-attention\n",
|
| 375 |
-
" attn_out = self.self_attn(src, src, src, mask=src_mask)\n",
|
| 376 |
-
" src = self.norm1(src + self.dropout(attn_out))\n",
|
| 377 |
-
" # Feed Forward\n",
|
| 378 |
-
" ff_out = self.ff(src)\n",
|
| 379 |
-
" src = self.norm2(src + self.dropout(ff_out))\n",
|
| 380 |
-
" return src\n",
|
| 381 |
-
"\n",
|
| 382 |
-
"class DecoderLayer(nn.Module):\n",
|
| 383 |
-
" def __init__(self, d_model, n_heads, dim_feedforward):\n",
|
| 384 |
-
" super(DecoderLayer, self).__init__()\n",
|
| 385 |
-
" self.self_attn = MultiHeadAttention(d_model, n_heads)\n",
|
| 386 |
-
" self.cross_attn = MultiHeadAttention(d_model, n_heads)\n",
|
| 387 |
-
" self.ff = FeedForward(d_model, dim_feedforward)\n",
|
| 388 |
-
"\n",
|
| 389 |
-
" self.norm1 = nn.LayerNorm(d_model)\n",
|
| 390 |
-
" self.norm2 = nn.LayerNorm(d_model)\n",
|
| 391 |
-
" self.norm3 = nn.LayerNorm(d_model)\n",
|
| 392 |
-
" self.dropout = nn.Dropout(0.1)\n",
|
| 393 |
-
"\n",
|
| 394 |
-
" def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):\n",
|
| 395 |
-
" # Self-attention (mask future tokens)\n",
|
| 396 |
-
" _tgt = tgt\n",
|
| 397 |
-
" tgt = self.norm1(tgt + self.dropout(self.self_attn(tgt, tgt, tgt, mask=tgt_mask)))\n",
|
| 398 |
-
" # Cross-attention\n",
|
| 399 |
-
" _tgt2 = tgt\n",
|
| 400 |
-
" tgt = self.norm2(tgt + self.dropout(self.cross_attn(tgt, memory, memory, mask=memory_mask)))\n",
|
| 401 |
-
" # Feed Forward\n",
|
| 402 |
-
" ff_out = self.ff(tgt)\n",
|
| 403 |
-
" tgt = self.norm3(tgt + self.dropout(ff_out))\n",
|
| 404 |
-
" return tgt\n",
|
| 405 |
-
"\n",
|
| 406 |
-
"class Encoder(nn.Module):\n",
|
| 407 |
-
" def __init__(self, vocab_size, d_model, n_heads, num_layers, dim_feedforward):\n",
|
| 408 |
-
" super(Encoder, self).__init__()\n",
|
| 409 |
-
" self.embedding = nn.Embedding(vocab_size, d_model)\n",
|
| 410 |
-
" self.pos_encoding = PositionalEncoding(d_model)\n",
|
| 411 |
-
" self.layers = nn.ModuleList([\n",
|
| 412 |
-
" EncoderLayer(d_model, n_heads, dim_feedforward)\n",
|
| 413 |
-
" for _ in range(num_layers)\n",
|
| 414 |
-
" ])\n",
|
| 415 |
-
"\n",
|
| 416 |
-
" def forward(self, src, src_mask=None):\n",
|
| 417 |
-
" # src shape: (batch_size, seq_len)\n",
|
| 418 |
-
" x = self.embedding(src) # (batch_size, seq_len, d_model)\n",
|
| 419 |
-
" x = self.pos_encoding(x)\n",
|
| 420 |
-
" for layer in self.layers:\n",
|
| 421 |
-
" x = layer(x, src_mask)\n",
|
| 422 |
-
" return x\n",
|
| 423 |
-
"\n",
|
| 424 |
-
"class Decoder(nn.Module):\n",
|
| 425 |
-
" def __init__(self, vocab_size, d_model, n_heads, num_layers, dim_feedforward):\n",
|
| 426 |
-
" super(Decoder, self).__init__()\n",
|
| 427 |
-
" self.embedding = nn.Embedding(vocab_size, d_model)\n",
|
| 428 |
-
" self.pos_encoding = PositionalEncoding(d_model)\n",
|
| 429 |
-
" self.layers = nn.ModuleList([\n",
|
| 430 |
-
" DecoderLayer(d_model, n_heads, dim_feedforward)\n",
|
| 431 |
-
" for _ in range(num_layers)\n",
|
| 432 |
-
" ])\n",
|
| 433 |
-
" self.fc_out = nn.Linear(d_model, vocab_size)\n",
|
| 434 |
-
"\n",
|
| 435 |
-
" def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):\n",
|
| 436 |
-
" x = self.embedding(tgt)\n",
|
| 437 |
-
" x = self.pos_encoding(x)\n",
|
| 438 |
-
" for layer in self.layers:\n",
|
| 439 |
-
" x = layer(x, memory, tgt_mask, memory_mask)\n",
|
| 440 |
-
" logits = self.fc_out(x) # (batch_size, seq_len, vocab_size)\n",
|
| 441 |
-
" return logits\n",
|
| 442 |
-
"\n",
|
| 443 |
-
"class TransformerSeq2Seq(nn.Module):\n",
|
| 444 |
-
" def __init__(self, src_vocab_size, tgt_vocab_size, d_model, n_heads, num_encoder_layers,\n",
|
| 445 |
-
" num_decoder_layers, dim_feedforward):\n",
|
| 446 |
-
" super(TransformerSeq2Seq, self).__init__()\n",
|
| 447 |
-
" self.encoder = Encoder(src_vocab_size, d_model, n_heads, num_encoder_layers, dim_feedforward)\n",
|
| 448 |
-
" self.decoder = Decoder(tgt_vocab_size, d_model, n_heads, num_decoder_layers, dim_feedforward)\n",
|
| 449 |
-
"\n",
|
| 450 |
-
" def forward(self, src, tgt, src_mask=None, tgt_mask=None):\n",
|
| 451 |
-
" # src: (batch_size, src_seq_len)\n",
|
| 452 |
-
" # tgt: (batch_size, tgt_seq_len)\n",
|
| 453 |
-
" memory = self.encoder(src, src_mask) # (batch_size, src_seq_len, d_model)\n",
|
| 454 |
-
" outputs = self.decoder(tgt, memory, tgt_mask) # (batch_size, tgt_seq_len, vocab_size)\n",
|
| 455 |
-
" return outputs"
|
| 456 |
-
],
|
| 457 |
-
"metadata": {
|
| 458 |
-
"id": "f8HioKcS2ZRy"
|
| 459 |
-
},
|
| 460 |
-
"execution_count": null,
|
| 461 |
-
"outputs": []
|
| 462 |
-
},
|
| 463 |
-
{
|
| 464 |
-
"cell_type": "code",
|
| 465 |
-
"source": [
|
| 466 |
-
"# ----------------------------\n",
|
| 467 |
-
"# 4. Training Setup\n",
|
| 468 |
-
"# ----------------------------\n",
|
| 469 |
-
"import torch\n",
|
| 470 |
-
"import torch.nn as nn\n",
|
| 471 |
-
"from torch.utils.data import DataLoader, TensorDataset\n",
|
| 472 |
-
"from typing import List, Tuple\n",
|
| 473 |
-
"import random\n",
|
| 474 |
-
"def generate_subsequent_mask(size):\n",
|
| 475 |
-
" # Mask out subsequent positions (for decoding)\n",
|
| 476 |
-
" mask = torch.triu(torch.ones(size, size), diagonal=1).bool()\n",
|
| 477 |
-
" return ~mask # True where we can attend, False where we cannot\n",
|
| 478 |
-
"\n",
|
| 479 |
-
"def train_one_epoch(model, optimizer, criterion, train_data, src_stoi, tgt_stoi):\n",
|
| 480 |
-
" model.train()\n",
|
| 481 |
-
" total_loss = 0\n",
|
| 482 |
-
" steps = 0\n",
|
| 483 |
-
"\n",
|
| 484 |
-
" data_loader = create_dataloader(train_pairs, src_stoi, tgt_stoi, BATCH_SIZE)\n",
|
| 485 |
-
" for src_batch, tgt_batch in data_loader:\n",
|
| 486 |
-
" src_batch = src_batch.to(DEVICE)\n",
|
| 487 |
-
" tgt_batch = tgt_batch.to(DEVICE)\n",
|
| 488 |
-
"\n",
|
| 489 |
-
" # Prepare the target inputs and outputs (shifted by one token)\n",
|
| 490 |
-
" tgt_inp = tgt_batch[:, :-1]\n",
|
| 491 |
-
" tgt_out = tgt_batch[:, 1:]\n",
|
| 492 |
-
"\n",
|
| 493 |
-
" # Create subsequent mask for the target sequence\n",
|
| 494 |
-
" tgt_seq_len = tgt_inp.size(1)\n",
|
| 495 |
-
" tgt_mask = generate_subsequent_mask(tgt_seq_len).to(DEVICE)\n",
|
| 496 |
-
"\n",
|
| 497 |
-
" optimizer.zero_grad()\n",
|
| 498 |
-
" logits = model(src_batch, tgt_inp, None, tgt_mask) # (B, seq_len, vocab_size)\n",
|
| 499 |
-
"\n",
|
| 500 |
-
" # Use .reshape() instead of .view() to avoid runtime errors\n",
|
| 501 |
-
" loss = criterion(logits.reshape(-1, logits.size(-1)), tgt_out.reshape(-1))\n",
|
| 502 |
-
" loss.backward()\n",
|
| 503 |
-
" optimizer.step()\n",
|
| 504 |
-
"\n",
|
| 505 |
-
" total_loss += loss.item()\n",
|
| 506 |
-
" steps += 1\n",
|
| 507 |
-
"\n",
|
| 508 |
-
" return total_loss / steps\n",
|
| 509 |
-
"\n",
|
| 510 |
-
"def evaluate(model, criterion, eval_data, src_stoi, tgt_stoi):\n",
|
| 511 |
-
" model.eval()\n",
|
| 512 |
-
" total_loss = 0\n",
|
| 513 |
-
" steps = 0\n",
|
| 514 |
-
" with torch.no_grad():\n",
|
| 515 |
-
" for src_batch, tgt_batch in create_batches(eval_data, src_stoi, tgt_stoi, BATCH_SIZE):\n",
|
| 516 |
-
" tgt_inp = tgt_batch[:, :-1]\n",
|
| 517 |
-
" tgt_out = tgt_batch[:, 1:]\n",
|
| 518 |
-
" tgt_seq_len = tgt_inp.size(1)\n",
|
| 519 |
-
" tgt_mask = generate_subsequent_mask(tgt_seq_len).to(DEVICE)\n",
|
| 520 |
-
"\n",
|
| 521 |
-
" logits = model(src_batch, tgt_inp, None, tgt_mask)\n",
|
| 522 |
-
" # Use .reshape() instead of .view()\n",
|
| 523 |
-
" loss = criterion(logits.reshape(-1, logits.size(-1)), tgt_out.reshape(-1))\n",
|
| 524 |
-
"\n",
|
| 525 |
-
" total_loss += loss.item()\n",
|
| 526 |
-
" steps += 1\n",
|
| 527 |
-
" return total_loss / steps\n",
|
| 528 |
-
"\n",
|
| 529 |
-
"def greedy_decode(model, src, src_stoi, tgt_stoi, tgt_itos, max_len=MAX_LEN):\n",
|
| 530 |
-
" \"\"\"\n",
|
| 531 |
-
" Given a single source sequence (1D list of token IDs),\n",
|
| 532 |
-
" generate a decoded target sequence using greedy search.\n",
|
| 533 |
-
" \"\"\"\n",
|
| 534 |
-
" model.eval()\n",
|
| 535 |
-
" src = torch.tensor(src, dtype=torch.long, device=DEVICE).unsqueeze(0) # (1, seq_len)\n",
|
| 536 |
-
" memory = model.encoder(src) # (1, seq_len, d_model)\n",
|
| 537 |
-
"\n",
|
| 538 |
-
" ys = torch.tensor([tgt_stoi[SOS_TOKEN]], dtype=torch.long, device=DEVICE).unsqueeze(0) # (1, 1)\n",
|
| 539 |
-
" for i in range(max_len-1):\n",
|
| 540 |
-
" tgt_mask = generate_subsequent_mask(ys.size(1)).to(DEVICE)\n",
|
| 541 |
-
" out = model.decoder(ys, memory, tgt_mask) # (1, seq_len, vocab_size)\n",
|
| 542 |
-
" prob = out[:, -1, :] # last timestep\n",
|
| 543 |
-
" next_token = torch.argmax(prob, dim=1).item()\n",
|
| 544 |
-
" ys = torch.cat([ys, torch.tensor([[next_token]], device=DEVICE)], dim=1)\n",
|
| 545 |
-
" if next_token == tgt_stoi[EOS_TOKEN]:\n",
|
| 546 |
-
" break\n",
|
| 547 |
-
"\n",
|
| 548 |
-
" # Convert back to tokens\n",
|
| 549 |
-
" out_tokens = ys.squeeze(0).tolist() # e.g. [SOS, ..., EOS]\n",
|
| 550 |
-
" # Remove the initial SOS\n",
|
| 551 |
-
" out_tokens = out_tokens[1:]\n",
|
| 552 |
-
" # Stop at EOS if present\n",
|
| 553 |
-
" if tgt_stoi[EOS_TOKEN] in out_tokens:\n",
|
| 554 |
-
" eos_idx = out_tokens.index(tgt_stoi[EOS_TOKEN])\n",
|
| 555 |
-
" out_tokens = out_tokens[:eos_idx]\n",
|
| 556 |
-
"\n",
|
| 557 |
-
" return \" \".join(tgt_itos[t] for t in out_tokens)"
|
| 558 |
-
],
|
| 559 |
-
"metadata": {
|
| 560 |
-
"id": "ffYgGSXy2a4B"
|
| 561 |
-
},
|
| 562 |
-
"execution_count": null,
|
| 563 |
-
"outputs": []
|
| 564 |
-
},
|
| 565 |
-
{
|
| 566 |
-
"cell_type": "code",
|
| 567 |
-
"source": [
|
| 568 |
-
"# ----------------------------\n",
|
| 569 |
-
"# 5. Main: Train the Model\n",
|
| 570 |
-
"# ----------------------------\n",
|
| 571 |
-
"if __name__ == \"__main__\":\n",
|
| 572 |
-
" # Hardcode the file paths from your GitHub repo (raw URLs):\n",
|
| 573 |
-
" train_path = \"https://raw.githubusercontent.com/asadsandhu/Pseudocode2Cpp/main/spoc/train/spoc-train.tsv\"\n",
|
| 574 |
-
" eval_path = \"https://raw.githubusercontent.com/asadsandhu/Pseudocode2Cpp/main/spoc/train/split/spoc-train-eval.tsv\"\n",
|
| 575 |
-
"\n",
|
| 576 |
-
" print(f\"Loading training data from {train_path} ...\")\n",
|
| 577 |
-
" train_pairs = load_spoc_data(train_path)\n",
|
| 578 |
-
" print(f\"Loaded {len(train_pairs)} training pairs.\")\n",
|
| 579 |
-
"\n",
|
| 580 |
-
" print(f\"Loading eval data from {eval_path} ...\")\n",
|
| 581 |
-
" eval_pairs = load_spoc_data(eval_path)\n",
|
| 582 |
-
" print(f\"Loaded {len(eval_pairs)} eval pairs.\")\n",
|
| 583 |
-
"\n",
|
| 584 |
-
" print(\"Building vocab...\")\n",
|
| 585 |
-
" src_stoi, src_itos, tgt_stoi, tgt_itos = build_vocab(train_pairs)\n",
|
| 586 |
-
" global stoi_eos\n",
|
| 587 |
-
" stoi_eos = tgt_stoi[EOS_TOKEN] # for pad_sequence usage\n",
|
| 588 |
-
"\n",
|
| 589 |
-
" print(\"Creating model...\")\n",
|
| 590 |
-
" model = TransformerSeq2Seq(\n",
|
| 591 |
-
" src_vocab_size=len(src_stoi),\n",
|
| 592 |
-
" tgt_vocab_size=len(tgt_stoi),\n",
|
| 593 |
-
" d_model=EMBED_DIM,\n",
|
| 594 |
-
" n_heads=NHEAD,\n",
|
| 595 |
-
" num_encoder_layers=NUM_ENCODER_LAYERS,\n",
|
| 596 |
-
" num_decoder_layers=NUM_DECODER_LAYERS,\n",
|
| 597 |
-
" dim_feedforward=FF_DIM\n",
|
| 598 |
-
" ).to(DEVICE)\n",
|
| 599 |
-
"\n",
|
| 600 |
-
" criterion = nn.CrossEntropyLoss(ignore_index=tgt_stoi[PAD_TOKEN])\n",
|
| 601 |
-
" optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
|
| 602 |
-
"\n",
|
| 603 |
-
" print(\"Starting training...\")\n",
|
| 604 |
-
" for epoch in range(1, EPOCHS+1):\n",
|
| 605 |
-
" train_loss = train_one_epoch(model, optimizer, criterion, train_pairs, src_stoi, tgt_stoi)\n",
|
| 606 |
-
" eval_loss = evaluate(model, criterion, eval_pairs, src_stoi, tgt_stoi)\n",
|
| 607 |
-
" print(f\"Epoch [{epoch}/{EPOCHS}] - Train Loss: {train_loss:.4f}, Eval Loss: {eval_loss:.4f}\")\n",
|
| 608 |
-
"\n",
|
| 609 |
-
" # Save model & vocab\n",
|
| 610 |
-
" torch.save({\n",
|
| 611 |
-
" 'model_state_dict': model.state_dict(),\n",
|
| 612 |
-
" 'src_stoi': src_stoi,\n",
|
| 613 |
-
" 'src_itos': src_itos,\n",
|
| 614 |
-
" 'tgt_stoi': tgt_stoi,\n",
|
| 615 |
-
" 'tgt_itos': tgt_itos\n",
|
| 616 |
-
" }, \"model.pth\")\n",
|
| 617 |
-
"\n",
|
| 618 |
-
" print(\"Model and vocab saved to model.pth\")"
|
| 619 |
-
],
|
| 620 |
-
"metadata": {
|
| 621 |
-
"colab": {
|
| 622 |
-
"base_uri": "https://localhost:8080/"
|
| 623 |
-
},
|
| 624 |
-
"id": "iffrMhkc2cVt",
|
| 625 |
-
"outputId": "38839989-38e5-4b10-fbea-90767dca60e3"
|
| 626 |
-
},
|
| 627 |
-
"execution_count": null,
|
| 628 |
-
"outputs": [
|
| 629 |
-
{
|
| 630 |
-
"output_type": "stream",
|
| 631 |
-
"name": "stdout",
|
| 632 |
-
"text": [
|
| 633 |
-
"Loading training data from https://raw.githubusercontent.com/asadsandhu/Pseudocode2Cpp/main/spoc/train/spoc-train.tsv ...\n",
|
| 634 |
-
"Loaded 293855 training pairs.\n",
|
| 635 |
-
"Loading eval data from https://raw.githubusercontent.com/asadsandhu/Pseudocode2Cpp/main/spoc/train/split/spoc-train-eval.tsv ...\n",
|
| 636 |
-
"Loaded 27289 eval pairs.\n",
|
| 637 |
-
"Building vocab...\n",
|
| 638 |
-
"Creating model...\n",
|
| 639 |
-
"Starting training...\n",
|
| 640 |
-
"Epoch [1/10] - Train Loss: 0.9915, Eval Loss: 0.4901\n",
|
| 641 |
-
"Epoch [2/10] - Train Loss: 0.4401, Eval Loss: 0.3597\n",
|
| 642 |
-
"Epoch [3/10] - Train Loss: 0.3326, Eval Loss: 0.2897\n",
|
| 643 |
-
"Epoch [4/10] - Train Loss: 0.2752, Eval Loss: 0.2735\n",
|
| 644 |
-
"Epoch [5/10] - Train Loss: 0.2401, Eval Loss: 0.2281\n",
|
| 645 |
-
"Epoch [6/10] - Train Loss: 0.2166, Eval Loss: 0.2111\n",
|
| 646 |
-
"Epoch [7/10] - Train Loss: 0.2002, Eval Loss: 0.2015\n",
|
| 647 |
-
"Epoch [8/10] - Train Loss: 0.1883, Eval Loss: 0.1919\n",
|
| 648 |
-
"Epoch [9/10] - Train Loss: 0.1793, Eval Loss: 0.1848\n",
|
| 649 |
-
"Epoch [10/10] - Train Loss: 0.1724, Eval Loss: 0.1819\n",
|
| 650 |
-
"Model and vocab saved to transformer_spoc.pth\n"
|
| 651 |
-
]
|
| 652 |
-
}
|
| 653 |
-
]
|
| 654 |
-
}
|
| 655 |
-
]
|
| 656 |
-
}
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