Diffusion-Based Coding Model with PyTorch

notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Diffusion-Based Coding Model with PyTorch\n",
        "This notebook provides a step-by-step guide to creating a diffusion-based coding model using PyTorch. It covers data collection from GitHub, preprocessing, data augmentation, defining a model architecture, training, evaluation, inference, and model saving/loading."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "import re\n",
        "import json\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import logging\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from torchtext.data.utils import get_tokenizer\n",
        "from torchtext.vocab import build_vocab_from_iterator\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Collection\n",
        "Collect code snippets and their corresponding prompts from GitHub repositories. In this example, we fetch code from a specified repository URL."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def fetch_code_snippets(repo_url):\n",
        "    response = requests.get(repo_url)\n",
        "    if response.status_code == 200:\n",
        "        soup = BeautifulSoup(response.content, 'html.parser')\n",
        "        code_snippets = []\n",
        "        for code_tag in soup.find_all('code'):\n",
        "            code_snippet = code_tag.get_text()\n",
        "            if code_snippet.strip():\n",
        "                code_snippets.append(code_snippet.strip())\n",
        "        return code_snippets\n",
        "    else:\n",
        "        raise Exception(f'Failed to fetch code snippets from {repo_url}')\n",
        "\n",
        "repo_url = 'https://github.com/example/repo'\n",
        "code_snippets = fetch_code_snippets(repo_url)\n",
        "print(f'Fetched {len(code_snippets)} code snippets.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Preprocessing\n",
        "Tokenize code snippets and convert them into sequences of tokens using a basic English tokenizer. In a real-world scenario, you would use a tokenizer that better understands code syntax."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def tokenize_code(code_snippet):\n",
        "    tokenizer = get_tokenizer('basic_english')\n",
        "    tokens = tokenizer(code_snippet)\n",
        "    return tokens\n",
        "\n",
        "def preprocess_data(code_snippets):\n",
        "    tokenized_snippets = [tokenize_code(snippet) for snippet in code_snippets]\n",
        "    all_tokens = [token for snippet in tokenized_snippets for token in snippet]\n",
        "    unique_tokens = list(set(all_tokens))\n",
        "    token_to_idx = {token: idx for idx, token in enumerate(unique_tokens)}\n",
        "    idx_to_token = {idx: token for token, idx in token_to_idx.items()}\n",
        "    return tokenized_snippets, token_to_idx, idx_to_token\n",
        "\n",
        "tokenized_snippets, token_to_idx, idx_to_token = preprocess_data(code_snippets)\n",
        "print(f'Total unique tokens: {len(token_to_idx)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Augmentation\n",
        "Enhance the training data to make the model more robust by applying random insertion, deletion, and swapping of tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def augment_data(tokenized_snippets, token_to_idx):\n",
        "    augmented_snippets = []\n",
        "    for snippet in tokenized_snippets:\n",
        "        # Random Insertion\n",
        "        if np.random.rand() < 0.1:\n",
        "            insertion_index = np.random.randint(0, len(snippet) + 1)\n",
        "            inserted_token = np.random.choice(list(token_to_idx.keys()))\n",
        "            snippet.insert(insertion_index, inserted_token)\n",
        "        # Random Deletion\n",
        "        if np.random.rand() < 0.1 and len(snippet) > 1:\n",
        "            deletion_index = np.random.randint(0, len(snippet))\n",
        "            del snippet[deletion_index]\n",
        "        # Random Swap\n",
        "        if np.random.rand() < 0.1 and len(snippet) > 1:\n",
        "            swap_index1, swap_index2 = np.random.choice(len(snippet), 2, replace=False)\n",
        "            snippet[swap_index1], snippet[swap_index2] = snippet[swap_index2], snippet[swap_index1]\n",
        "        augmented_snippets.append(snippet)\n",
        "    return augmented_snippets\n",
        "\n",
        "augmented_snippets = augment_data(tokenized_snippets, token_to_idx)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model Architecture\n",
        "Define a simple diffusion model architecture using PyTorch. In practice, a diffusion-based model would include iterative denoising steps; here we illustrate a basic model structure that can be extended with diffusion-specific components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "class DiffusionModel(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim):\n",
        "        super(DiffusionModel, self).__init__()\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        # In a full diffusion model, this would be replaced by a Transformer-based denoiser with time-step conditioning\n",
        "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_dim, vocab_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.embedding(x)\n",
        "        x, _ = self.lstm(x)\n",
        "        x = self.fc(x)\n",
        "        return x\n",
        "\n",
        "vocab_size = len(token_to_idx)\n",
        "embedding_dim = 256\n",
        "hidden_dim = 512\n",
        "learning_rate = 0.001\n",
        "\n",
        "model = DiffusionModel(vocab_size, embedding_dim, hidden_dim)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Preparation\n",
        "Convert tokens to indices and create batches for training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "class CodeDataset(Dataset):\n",
        "    def __init__(self, tokenized_snippets, token_to_idx, seq_length=100):\n",
        "        self.tokenized_snippets = tokenized_snippets\n",
        "        self.token_to_idx = token_to_idx\n",
        "        self.seq_length = seq_length\n",
        "    def __len__(self):\n",
        "        return len(self.tokenized_snippets)\n",
        "    def __getitem__(self, idx):\n",
        "        snippet = self.tokenized_snippets[idx]\n",
        "        input_seq = snippet[:self.seq_length]\n",
        "        target_seq = snippet[1:self.seq_length+1]\n",
        "        input_seq = [self.token_to_idx[token] for token in input_seq]\n",
        "        target_seq = [self.token_to_idx[token] for token in target_seq]\n",
        "        return torch.tensor(input_seq), torch.tensor(target_seq)\n",
        "\n",
        "seq_length = 100\n",
        "batch_size = 32\n",
        "\n",
        "dataset = CodeDataset(augmented_snippets, token_to_idx, seq_length)\n",
        "train_dataset, val_dataset = train_test_split(dataset, test_size=0.2, random_state=42)\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training\n",
        "Train the diffusion model using the prepared data. In a complete diffusion model, you would incorporate a noise schedule and iterative denoising. Here, we demonstrate a simple training loop."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "num_epochs = 10\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    for inputs, targets in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs.view(-1, vocab_size), targets.view(-1))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    scheduler.step()\n",
        "    avg_loss = total_loss / len(train_loader)\n",
        "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluation\n",
        "Evaluate the model on the validation set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.eval()\n",
        "total_loss = 0\n",
        "with torch.no_grad():\n",
        "    for inputs, targets in val_loader:\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs.view(-1, vocab_size), targets.view(-1))\n",
        "        total_loss += loss.item()\n",
        "\n",
        "val_loss = total_loss / len(val_loader)\n",
        "print(f'Validation Loss: {val_loss}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Inference\n",
        "Generate code snippets using the trained model. This function takes a prompt and generates code by iteratively predicting the next token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def generate_code(model, prompt, max_length=100):\n",
        "    model.eval()\n",
        "    input_seq = [token_to_idx[token] for token in tokenize_code(prompt)]\n",
        "    input_seq = torch.tensor(input_seq).unsqueeze(0)\n",
        "    generated_code = []\n",
        "    with torch.no_grad():\n",
        "        for _ in range(max_length):\n",
        "            outputs = model(input_seq)\n",
        "            _, predicted = torch.max(outputs[:, -1, :], 1)\n",
        "            generated_code.append(idx_to_token[predicted.item()])\n",
        "            input_seq = torch.cat([input_seq, predicted.unsqueeze(0)], dim=1)\n",
        "    return ' '.join(generated_code)\n",
        "\n",
        "prompt = 'def hello_world():'\n",
        "generated_code = generate_code(model, prompt)\n",
        "print(generated_code)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model Saving and Loading\n",
        "Save and load the trained model for later use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def save_model(model, path):\n",
        "    torch.save(model.state_dict(), path)\n",
        "\n",
        "def load_model(model, path):\n",
        "    model.load_state_dict(torch.load(path))\n",
        "    model.eval()\n",
        "\n",
        "model_path = 'diffusion_model.pth'\n",
        "save_model(model, model_path)\n",
        "load_model(model, model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Error Handling and Logging\n",
        "Robust error handling and logging to track training and inference processes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
        "\n",
        "try:\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        total_loss = 0\n",
        "        for inputs, targets in train_loader:\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs.view(-1, vocab_size), targets.view(-1))\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            total_loss += loss.item()\n",
        "        scheduler.step()\n",
        "        avg_loss = total_loss / len(train_loader)\n",
        "        logging.info(f'Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss}')\n",
        "except Exception as e:\n",
        "    logging.error(f'An error occurred: {e}')"
      ]
    }
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