llm trainer (#12)

Author: ckrause

loda/ml/llm/README.md

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+# LODA LLM: Natural Language to Assembly Code Generation
+
+This module extends the LODA Python project with Large Language Model (LLM) capabilities for generating LODA assembly code from natural language descriptions of integer sequences.
+
+## Overview
+
+The LODA LLM system can understand descriptions like "Fibonacci numbers" or "squares of positive integers" and generate corresponding LODA assembly programs that compute these sequences.
+
+### Key Features
+
+- **Transformer-based Architecture**: Uses T5 encoder-decoder model for sequence-to-sequence translation
+- **OEIS Integration**: Trained on 145,000+ OEIS sequence descriptions and LODA programs  
+- **Robust Preprocessing**: Extracts and augments training data from existing LODA programs
+- **Comprehensive Evaluation**: Validates generated programs and evaluates sequence correctness
+- **Interactive Interface**: Command-line tool for real-time code generation
+
+## Architecture
+
+```
+Natural Language → T5 Encoder → Hidden Representation → T5 Decoder → LODA Code
+     ↓                                                                    ↓
+"Fibonacci numbers"                                              "mov $1,$0\n..."
+```
+
+### Components
+
+1. **Data Preprocessing** (`data_preprocessing.py`)
+   - Extracts sequence descriptions from LODA program comments
+   - Creates training pairs of (description, LODA code)
+   - Augments data with description variations
+   - Handles data cleaning and validation
+
+2. **Model Architecture** (`model.py`)
+   - T5-based encoder-decoder transformer
+   - Custom LODA tokenizer for assembly syntax
+   - Text format conversion for T5 compatibility
+   - Model saving/loading utilities
+
+3. **Training Pipeline** (`trainer.py`)
+   - PyTorch training loop with proper batching
+   - Learning rate scheduling and gradient clipping
+   - Validation and checkpointing
+   - Support for different T5 model sizes
+
+4. **Inference & Evaluation** (`inference.py`)
+   - Code generation from natural language
+   - Program validation and sequence testing
+   - Evaluation metrics (validity, accuracy)
+   - Interactive generation interface
+
+## Installation
+
+1. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+2. The new LLM dependencies include:
+   - `torch>=1.9.0` - PyTorch for deep learning
+   - `transformers>=4.20.0` - Hugging Face transformers (T5)
+   - `datasets>=2.0.0` - Data loading utilities
+   - `tqdm>=4.62.0` - Progress bars
+   - `scikit-learn>=1.0.0` - Evaluation metrics
+
+## Usage
+
+### 1. Prepare Training Data
+
+```python
+from loda.ml.llm.data_preprocessing import create_dataset
+
+# Create training dataset from OEIS programs
+dataset = create_dataset(
+    programs_dir="programs/oeis",
+    output_file="loda_training_data.json",
+    max_examples=10000,  # Use subset for faster training
+    augment=True  # Create description variations
+)
+```
+
+### 2. Train the Model
+
+```python
+from loda.ml.llm.trainer import train_loda_llm
+
+# Train the model
+model = train_loda_llm(
+    programs_dir="programs/oeis",
+    output_dir="trained_model",
+    model_name="t5-small",  # or "t5-base", "t5-large"
+    max_examples=10000,
+    num_epochs=3,
+    batch_size=8
+)
+```
+
+Command line training:
+```bash
+python -m loda.ml.llm.trainer \
+    --programs_dir programs/oeis \
+    --output_dir trained_model \
+    --max_examples 10000 \
+    --num_epochs 3
+```
+
+### 3. Generate Code
+
+```python
+from loda.ml.llm.inference import load_model_for_inference
+
+# Load trained model
+generator = load_model_for_inference("trained_model")
+
+# Generate code
+results = generator.generate("Fibonacci numbers")
+for result in results:
+    print(f"Generated: {result.generated_code}")
+    print(f"Valid: {result.is_valid}")
+    if result.generated_sequence:
+        print(f"Sequence: {result.generated_sequence}")
+```
+
+Interactive mode:
+```bash
+python -m loda.ml.llm.inference --mode interactive --model_path trained_model
+```
+
+### 4. Evaluate Performance
+
+```python
+from loda.ml.llm.inference import evaluate_model
+
+# Evaluate on test set
+metrics, results = evaluate_model("trained_model", "test_data.json")
+print(f"Valid program rate: {metrics['valid_program_rate']:.1%}")
+print(f"Sequence match rate: {metrics['sequence_match_rate']:.1%}")
+```
+
+## Training Data Format
+
+Training examples are JSON objects with the following structure:
+
+```json
+{
+  "sequence_id": "A000045",
+  "description": "Fibonacci numbers: F(n) = F(n-1) + F(n-2) with F(0) = 0 and F(1) = 1",
+  "loda_code": "mov $1,$0\nmov $4,1\nlpb $0\n...",
+  "terms": [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
+}
+```
+
+## Model Configuration
+
+### Supported T5 Models
+
+- `t5-small` (60M parameters) - Fast training, good for experimentation
+- `t5-base` (220M parameters) - Better quality, moderate resource requirements  
+- `t5-large` (770M parameters) - Best quality, high resource requirements
+
+### Training Parameters
+
+```python
+# Recommended settings for different use cases
+
+# Quick experimentation
+train_loda_llm(
+    model_name="t5-small",
+    max_examples=1000,
+    batch_size=16,
+    num_epochs=1,
+    learning_rate=1e-4
+)
+
+# Production training
+train_loda_llm(
+    model_name="t5-base", 
+    max_examples=-1,  # Use all data
+    batch_size=8,
+    num_epochs=5,
+    learning_rate=5e-5
+)
+```
+
+## Evaluation Metrics
+
+The system provides several evaluation metrics:
+
+- **Valid Program Rate**: Percentage of generated programs that parse and execute
+- **Exact Match Rate**: Percentage matching the target program exactly
+- **Sequence Match Rate**: Percentage generating correct sequence terms
+- **Generation Time**: Average time to generate code
+
+## Implementation Details
+
+### LODA Tokenization
+
+The system uses a custom tokenizer designed for LODA assembly:
+
+```python
+# LODA operations
+operations = ['mov', 'add', 'sub', 'mul', 'div', 'lpb', 'lpe', ...]
+
+# Memory operands  
+operands = ['$0', '$1', '$2', '$$1', '$$2', ...]
+
+# Constants
+constants = ['0', '1', '2', '-1', ...]
+```
+
+### Text Format Conversion
+
+Since T5 expects text input/output, LODA code is converted to a text representation:
+
+```
+Original LODA:     mov $1,$0
+                   add $1,5
+                   
+Text format:       mov $1 $0 | add $1 5
+```
+
+### Data Augmentation
+
+Training descriptions are augmented to improve robustness:
+
+```
+Original: "Fibonacci numbers"
+Augmented: 
+- "Sequence of fibonacci numbers"
+- "Generate fibonacci numbers"  
+- "Compute fibonacci numbers"
+```
+
+## Performance Considerations
+
+### Memory Usage
+
+- T5-small: ~2GB GPU memory for training
+- T5-base: ~8GB GPU memory for training
+- T5-large: ~16GB GPU memory for training
+
+### Training Time
+
+Approximate training times (on V100 GPU):
+- 1,000 examples: 10-30 minutes
+- 10,000 examples: 2-6 hours  
+- 100,000+ examples: 1-3 days
+
+### Generation Speed
+
+- T5-small: ~0.1-0.5 seconds per program
+- T5-base: ~0.2-1.0 seconds per program
+- T5-large: ~0.5-2.0 seconds per program
+
+## Troubleshooting
+
+### Common Issues
+
+1. **CUDA out of memory**: Reduce batch size or use smaller model
+2. **Poor generation quality**: Train longer or use larger model
+3. **Invalid programs**: Check training data quality and augmentation
+
+### Model Selection
+
+Choose model size based on your requirements:
+
+| Use Case | Model | Trade-offs |
+|----------|-------|------------|
+| Research/Experimentation | t5-small | Fast, lower quality |
+| Production/Demo | t5-base | Balanced speed/quality |
+| Best Results | t5-large | Slow, highest quality |
+
+## Extending the System
+
+### Custom Training Data
+
+Add new training examples:
+
+```python
+from loda.ml.llm.data_preprocessing import TrainingExample
+
+custom_example = TrainingExample(
+    sequence_id="custom_001",
+    description="Powers of 2", 
+    loda_code="mov $1,1\nlpb $0\n  mul $1,2\n  sub $0,1\nlpe\nmov $0,$1",
+    terms=[1, 2, 4, 8, 16, 32]
+)
+```
+
+### Fine-tuning
+
+Fine-tune on specific sequence types:
+
+```python
+# Load pre-trained model
+model = LodaT5Model.load_model("base_model")
+
+# Train on specialized data
+train_loda_llm(
+    programs_dir="specialized_programs",
+    model=model,  # Start from pre-trained
+    learning_rate=1e-5,  # Lower learning rate
+    num_epochs=1
+)
+```
+
+## Future Improvements
+
+- **Better tokenization**: Domain-specific vocabulary
+- **Program synthesis**: Multi-step reasoning
+- **Verification**: Formal correctness checking  
+- **Interactive refinement**: Human-in-the-loop generation
+- **Specialized architectures**: CodeBERT, CodeT5+ integration
+
+---
+
+For more information, see the LODA project documentation and the individual module docstrings.

loda/ml/llm/__init__.py

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+"""
+Large Language Model (LLM) implementation for natural language to LODA code generation.
+
+This module provides functionality to train transformer-based models that can understand
+natural language descriptions of integer sequences (like OEIS sequences) and generate
+corresponding LODA assembly programs.
+
+Key components:
+- Data preprocessing for OEIS sequence descriptions and LODA programs
+- Transformer-based encoder-decoder architecture
+- Training pipeline with proper tokenization
+- Inference utilities for code generation
+- Evaluation metrics for generated programs
+
+Example usage:
+>>> from loda.ml.llm import LodaT5Model, LodaGenerator, train_loda_llm
+>>> 
+>>> # Train a model
+>>> model = train_loda_llm("programs/oeis", "trained_model")
+>>> 
+>>> # Generate code
+>>> generator = LodaGenerator(model)
+>>> results = generator.generate("Fibonacci numbers")
+>>> print(results[0].generated_code)
+"""
+
+# Import main classes for easy access
+# Handle optional dependencies gracefully
+try:
+    from .model import LodaT5Model, LodaTokenizer
+    from .trainer import LodaTrainer, train_loda_llm
+    from .inference import LodaGenerator, LodaEvaluator, GenerationResult
+    _llm_available = True
+except ImportError:
+    _llm_available = False
+    # Create placeholder classes
+    class _MissingDependency:
+        def __init__(self, *args, **kwargs):
+            raise ImportError(
+                "LLM functionality requires additional dependencies. "
+                "Install with: pip install torch transformers datasets tqdm"
+            )
+    
+    LodaT5Model = _MissingDependency
+    LodaTokenizer = _MissingDependency
+    LodaTrainer = _MissingDependency
+    train_loda_llm = _MissingDependency
+    LodaGenerator = _MissingDependency
+    LodaEvaluator = _MissingDependency
+    GenerationResult = _MissingDependency
+
+# Data preprocessing doesn't require PyTorch/transformers
+from .data_preprocessing import DataPreprocessor, TrainingExample, create_dataset
+
+__all__ = [
+    'LodaT5Model',
+    'LodaTokenizer', 
+    'LodaTrainer',
+    'train_loda_llm',
+    'LodaGenerator',
+    'LodaEvaluator',
+    'GenerationResult',
+    'DataPreprocessor',
+    'TrainingExample',
+    'create_dataset',
+    '_llm_available'
+]