Titan Memory-Integrated Llama-2 for Code Generation

This document outlines how to train a Llama-2-based Large Language Model (LLM) with a custom “Titan” memory module, which persistently maintains and updates a learned representation of prior context. The overall goal is to build a coding assistant that can generate code while leveraging an external memory to adapt over time.

Note: This project is a proof-of-concept to demonstrate how one might integrate persistent neural memory into a causal language model. Achieving state-of-the-art code generation (comparable to tools like Copilot, Code Llama, StarCoder, etc.) requires extensive data, compute resources, and additional fine-tuning strategies. Nonetheless, this repository shows a framework to get started.

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Table of Contents

1. Project Structure
2. Key Features
3. Usage & Setup
   1. Environment Setup
   2. Running the Notebook or Script
4. Components Explanation
   1. NeuralMemory
   2. LlamaTitanModel
   3. Training Pipeline
5. Customization & Extensions
6. Performance & Scaling
7. Limitations
8. License & Acknowledgments

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Project Structure

.
├── train.py    # Example script/notebook showcasing the full training flow
├── README.md                     # This README
└── requirements.txt              # (Optional) If you choose to define your environment dependencies

• TitanMemoryLLM_Training.py (or a notebook version) contains the end-to-end training pipeline.
• README.md (this file) explains the conceptual flow and usage.
• requirements.txt is optional; you might define the environment dependencies there if you want to replicate the environment easily.

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Key Features

1. Llama-2 Causal Language Modeling

   • Uses LlamaForCausalLM from Hugging Face Transformers to provide next-token prediction for code or text.

2. Titan Memory Module

   • A custom NeuralMemory class keeps a persistent buffer of size MEMORY_SIZE.
   • Each forward pass, it computes a “surprise” measure based on hidden-state differences and updates this buffer.
   • Integrates memory-augmented representations back into the LLM’s hidden states.

3. Hugging Face Accelerate

   • Handles distributed training, mixed precision, or multi-GPU setups with minimal changes to the code.
   • Simplifies device handling and gradient synchronization.

4. MLflow Integration

   • Tracks hyperparameters, training loss, and other metrics.
   • Saves experiment logs so you can compare across runs.

5. Built for Code Datasets

   • Integrates with code-related datasets like CodeSearchNet, The Stack, or Apps from Hugging Face.
   • Demonstrates typical steps for text preprocessing and labeled data (if relevant).

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Usage & Setup

Environment Setup

1. Python Environment

   • Python 3.8+ is recommended (though 3.9+ also works).
   • You need a GPU with substantial VRAM for training a 7B+ model (e.g., NVIDIA A100 with 40GB, or multiple GPUs using Accelerate).

2. Install Dependencies

   • If running in a Databricks notebook, you can prepend each cell with %pip install ... or run directly:

     pip install torch torchvision transformers datasets tokenizers accelerate deepspeed mlflow

   • Alternatively, define them in requirements.txt and run:

     pip install -r requirements.txt

3. Model Checkpoint

   • You must have access to meta-llama/Llama-2-7b-hf (or a similar Llama-2 checkpoint) on Hugging Face. This typically requires acceptance of the license terms.

Running the Notebook or Script

1. Databricks Notebook

   • Upload the .py or .ipynb file to your Databricks workspace.
   • Attach to a GPU cluster (with at least one A100, ideally).
   • Update any configuration paths (e.g., CHECKPOINT_DIR, LOGGING_DIR) to point to your preferred storage.
   • Run all cells in order.

2. Local / Server Training

   • Make sure you have GPUs accessible locally or on a remote server with CUDA.
   • Clone or copy this repository.
   • Modify the paths (e.g., CHECKPOINT_DIR) in the config class.
   • Run:

     python TitanMemoryLLM_Training.py

Tip: If you are memory-constrained, reduce BATCH_SIZE or MAX_LENGTH to avoid out-of-memory (OOM) errors.

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Components Explanation

1. NeuralMemory

• Location: Defined inside the script/notebook under class NeuralMemory(nn.Module).

• Function:

  • Maintains a persistent buffer (self.memory) with shape (MEMORY_SIZE, HIDDEN_DIM).
  • Projects the model’s hidden states into a memory space, computes a “surprise” measure by comparing them to the persistent buffer, and updates the buffer.
  • Returns a memory-augmented hidden state that is later combined with the model’s own representation.

• Key Methods:

  • forward(hidden_states):
    1. Projects hidden_states into memory space.
    2. Computes difference (L2 norm) with the persistent memory.
    3. Calculates “surprise” as average difference across memory slots.
    4. Updates memory outside the gradient flow using a weighted average of past memory and the new states.
    5. Returns the final memory-augmented representation.

• Design Note: This approach is simplistic. For large-scale usage, you may need more advanced gating or a recurrent-like memory for stable gradient-based learning.

2. LlamaTitanModel

• Location: class LlamaTitanModel(nn.Module).

• Function:

  • Wraps LlamaForCausalLM (the standard Llama-2 architecture for next-token prediction).
  • Integrates the NeuralMemory after obtaining the base hidden states from Llama.
  • Applies an optional MultiheadAttention to blend the original hidden states with the memory outputs.
  • Finally, passes the resulting representation to the Llama language modeling head to get output logits.

• Key Steps:

  1. Get hidden states from self.llama_model.model(...).
  2. Pass them to self.memory_module(...).
  3. Combine via self.memory_attention(...).
  4. Generate logits via self.llama_model.lm_head(...).
  5. Compute a causal language modeling loss if labels are provided.

3. Training Pipeline

1. Data Loading:

   • Uses load_dataset from Hugging Face to fetch code-related datasets (code_search_net, bigcode/the-stack, codeparrot/apps).
   • Concatenates them.
   • Tokenizes up to MAX_LENGTH tokens, sets labels = input_ids for causal LM.

2. Accelerate Setup:

   • Creates an Accelerator instance.
   • Prepares model, optimizer, and dataloader for distributed or mixed-precision training.

3. Forward & Backward Pass:

   • Each iteration, calls model(input_ids, attention_mask, labels).
   • Loss is backpropagated, and steps are accumulated until GRADIENT_ACCUMULATION_STEPS is reached.
   • Optimizer and scheduler are stepped, and gradients are zeroed out.

4. MLflow Logging:

   • Logs hyperparameters at the start of the run.
   • Logs training loss at each epoch step.
   • (Optional) Add custom logs or validation metrics as needed.

5. Checkpointing:

   • After all epochs, calls accelerator.unwrap_model(model) to get the base model.
   • Saves it to CHECKPOINT_DIR using save_pretrained.

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Customization & Extensions

1. Partial Freezing:

   • Uncomment lines in LlamaTitanModel.__init__ to freeze early layers if you only want to train the memory and final layers.

2. Different Datasets:

   • Change config.DATASET_NAMES to your target code or text datasets.
   • Ensure the tokenize_function reflects the correct column names (e.g., 'code', 'text', etc.).

3. Evaluation & Validation:

   • Create a separate validation set or reserve part of your dataset.
   • Periodically evaluate perplexity or generation quality on the validation set.

4. Memory Mechanism:

   • Replace the simplistic memory update logic with a more advanced approach (e.g., gating, trainable memory parameters, or recurrent-like memory).

5. Model Size:

   • Switch to “meta-llama/Llama-2-13b-hf” or “meta-llama/Llama-2-70b-hf” if you have the necessary hardware resources and you want a bigger foundation.
   • For smaller experiments or prototyping, consider “meta-llama/Llama-2-7b-hf” or even a smaller code-specific base.

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Performance & Scaling

• GPU Requirements:

  • A single 7B model with MAX_LENGTH=1024 can use 20–30 GB of VRAM or more, depending on batch size.
  • To handle multi-GPU setups, ensure you use Accelerate in distributed mode, or run on a cluster (e.g., Databricks with multiple A100s).

• Gradient Checkpointing:

  • If you encounter out-of-memory errors, enable model.llama_model.gradient_checkpointing_enable() or reduce MAX_LENGTH/BATCH_SIZE.

• Throughput:

  • Training code LLMs can be slow. Consider further optimizations or distributed training strategies.

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Limitations

1. Memory Implementation:

   • The NeuralMemory uses a simple in-place update that is mostly outside the standard gradient flow. This might limit the effectiveness of the memory.
   • A more robust approach could use a differentiable memory with trainable parameters and gating mechanisms.

2. Data Volume:

   • Public code datasets can be extremely large (e.g., The Stack). For production usage, you may want to stream the data or use dataset subsets.

3. Evaluation & RLHF:

   • This script does not implement a reward model or RLHF, which are often crucial for refining code generation quality and instruction-following behavior.

4. Licensing & Access:

   • Access to Llama-2 checkpoints requires agreement to Meta’s license. Ensure you have permissions.

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License & Acknowledgments

• Llama-2 Model: Provided by Meta under its Llama-2 license terms.
• Transformers & Datasets: By Hugging Face, under Apache 2.0.
• MLflow: An open-source platform for managing ML lifecycles.
• Accelerate: An open-source library by Hugging Face for easy distributed and mixed-precision training.