python

CMR Python Documentation

Welcome to the documentation for the Contextual Memory Reweaving (CMR) Python implementation. This documentation provides comprehensive information about all modules, components, and functionality within the CMR system.

Overview

Table of Contents

• Overview
• Key Features
• Quick Start
• Architecture Overview
• Module Documentation
• Getting Started
• Configuration Guide
• Performance Considerations
• Best Practices
• Troubleshooting
• Contributing
• Version Information
• License

The CMR system is a sophisticated memory-enhanced language model architecture that enables contextual memory reweaving for improved performance on long-context tasks. The system integrates advanced memory management, retrieval strategies, and optimization techniques with transformer-based language models.

Key Features

• Persistent Memory: Long-term storage and retrieval of contextual information
• Adaptive Retrieval: Intelligent memory recall based on relevance and context
• Layered Architecture: Modular design for flexible integration with existing models
• Performance Optimization: Efficient memory management and computation
• Model Agnostic: Works with various transformer architectures
• Extensible Design: Easy to extend with custom components and strategies

Memory Reweaving

Memory Reweaving helps a model remember and reuse the right pieces of past context at the right time—without forcing you to repeat yourself. Think of it like a well‑organized notebook for a digital assistant: it notices important details, files them neatly, and brings them back when they matter.

What it is (in plain terms)

• Selective memory: The system keeps short highlights of what mattered in previous interactions instead of every word.
• Layered storage: Recent notes stay close by, important facts move to a more durable shelf, and rare but critical items go into long‑term storage.
• Timely recall: When you ask something new, CMR fetches only the most relevant notes and weaves them into the model’s thinking.

Why this works

• Long chats and large documents overwhelm a model if we resend everything each time.
• Most details aren’t needed later—but the few that are can be decisive.
• CMR captures, scores, and retrieves those decisive details automatically.

How it works (step by step)

1. Notice: While processing text, the system identifies potentially useful signals (facts, preferences, task steps).
2. Store: These signals are turned into compact entries and placed into a layered memory.
3. Score: Each entry is scored for relevance, recency, and usefulness.
4. Retrieve: For a new request, only top‑scoring, context‑matching entries are pulled back.
5. Reweave: Retrieved entries are blended with the current input to guide the response.
6. Maintain: Old or low‑value entries are pruned; important ones can be kept longer.

Visual: Memory flow at a glance

flowchart LR
    U[User Input and New Task] --> C[Capture and Score]
    C --> MB[Layered Memory]
    subgraph MB2 [Layered Memory]
        RBUF[Recent Buffer]
        WMEM[Working Memory]
        LTM[Long term Memory]
    end
    MB --> MB2
    Q[Current Query] --> RETR[Retrieve Relevant]
    MB2 --> RETR
    RETR --> REWEAVE[Reweave with Current Context]
    REWEAVE --> OUT[Model Output]

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Visual: The memory layers

graph TD
  subgraph Layered Memory
    A[Recent Buffer]
    B[Working Memory]
    C[Long term Memory]
  end
  A --> B --> C

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Everyday examples

• Customer support assistant

  • Learns: your device type and subscription plan.
  • Reuses later: suggests the right troubleshooting steps automatically.

• Writing copilot

  • Learns: your project name and tone (e.g., “concise, friendly”).
  • Reuses later: drafts content in the same voice without reminders.

• Banking helper

  • Learns: your “every other Friday” payday and preferred transfer account.
  • Reuses later: when you say “schedule my usual transfer this week,” it proposes the right amount and account, asking only for confirmation.

Advantages

• Personal continuity: Remembers preferences and prior decisions, reducing back‑and‑forth.
• Less repetition: You don’t need to restate context every time.
• Focused context: Sends only what’s relevant to the model, not the entire history.
• Better long‑task reliability: Maintains coherence over multi‑step workflows.
• Inspectable memory: Entries are structured and can be reviewed or pruned.
• Privacy controls: Sensitive items can be excluded or kept ephemeral.
• Model‑agnostic: Works alongside different transformer backbones.

Controls you can enable

• Relevance thresholds: Only keep or recall entries above a chosen score.
• Eviction policies: Automatically clean up stale or low‑value entries.
• Retrieval budgets: Limit how many items can be pulled back per request.
• Opt‑out flags: Mark specific content as non‑persistent when needed.

What to expect

• Fewer clarifying questions from the assistant.
• More consistent tone and intent across sessions.
• Improved accuracy on tasks that span many steps or documents.

Quick Start

Minimal, offline-friendly setup using the lightweight transformer with hooks:

from types import SimpleNamespace
import torch
from models import CMRTransformer

# Minimal config: only fields used by CMRTransformer are required
base_cfg = SimpleNamespace(vocab_size=50257, num_hidden_layers=8)

# Memory configuration
memory_config = {"target_layers": [2, 4], "buffer_size": 3}

# Create model and register hooks
cmr = CMRTransformer(config=base_cfg, memory_config=memory_config)
cmr.register_memory_hooks()
cmr.enable_memory()

# Run a tiny forward pass
input_ids = torch.randint(0, base_cfg.vocab_size, (1, 16))  # [batch=1, seq=16]
outputs = cmr(input_ids)

# Inspect memory stats
print(outputs["memory_stats"])  # {'total_captured_states': ..., 'layers_with_memory': [...]} 

Architecture Overview

classDiagram
    class CMRSystem {
        +LayeredMemoryBuffer memory_buffer
        +RelevanceScorer scorer
        +AdvancedMemoryRetriever retriever
        +LayeredStateReconstructor reconstructor
        +enable_memory()
        +disable_memory()
        +forward()
    }
  
    class LayeredMemoryBuffer {
        +add_entry()
        +retrieve_recent()
        +retrieve_relevant()
    }
  
    CMRSystem --> LayeredMemoryBuffer
    CMRSystem --> RelevanceScorer
    CMRSystem --> AdvancedMemoryRetriever
    CMRSystem --> LayeredStateReconstructor
  
    MistralAdapter --|> BackboneAdapter
    GemmaAdapter --|> BackboneAdapter

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Core Modules

Module	Description	Status
Models	Core neural networks and data structures	✅ Active
Integration	System orchestration and coordination	✅ Active
Services	Advanced retrieval and processing	🔄 In Development
Monitoring	Performance tracking and metrics	✅ Active
Optimization	Performance enhancement	🔄 In Development
Utils	Infrastructure and helpers	✅ Active

Module Documentation

Models Module

The models module contains the core neural network models and data structures:

Key Components:

• Base Transformer: Enhanced transformer with memory capabilities
• Memory Entry: Core data structure for stored memories
• Relevance Scorer: Determines importance of hidden states
• Advanced Retrieval: Minimal top‑k retrieval today; advanced strategies planned
• Performance Optimization: Placeholder (stub); features planned
• Integrated Models: FullCMRModel is available; Integrated CMR Model planned

Primary Use Cases:

• Building memory-enhanced language models
• Implementing custom retrieval strategies
• Optimizing model performance
• Integrating with existing transformer architectures

Integration Module

The integration module provides system orchestration and coordination:

Key Components:

• Integrated CMR Model (Planned): Foundational CMR integration
• Full CMR Model: Complete CMR implementation
• Mistral Integration: Specialized Mistral model integration
• Hook System: Comprehensive hook management
• Component Coordination: Seamless component integration

Primary Use Cases:

• Deploying complete CMR systems
• Integrating CMR with existing models
• Coordinating multiple CMR components
• Production-ready CMR implementations

Experiments Module

The experiments module provides comprehensive testing and analysis frameworks:

Key Components:

• Dataset Testing: Multi-dataset evaluation framework
• Performance Analysis: Detailed performance analysis tools
• Benchmarking: Comprehensive benchmarking capabilities
• Visualization: Results visualization and reporting
• Comparative Analysis: Cross-configuration comparisons

Primary Use Cases:

• Evaluating CMR performance
• Benchmarking different configurations
• Analyzing system behavior
• Generating performance reports

Monitoring Module

The monitoring module provides performance monitoring and metrics collection:

Key Components:

• Performance Monitor: Real-time performance tracking
• Health Monitoring: System health assessment
• Metrics Collection: Comprehensive metrics gathering
• Diagnostic Tools: System diagnostic capabilities
• Alert System: Performance alert mechanisms

Primary Use Cases:

• Monitoring system performance
• Tracking system health
• Collecting performance metrics
• Diagnosing system issues

Optimization Module

The optimization module provides performance enhancement capabilities:

Key Components:

• Adaptive Thresholds: Dynamic threshold management
• Batch Optimization: Batch processing optimization
• Memory Prefetching: Predictive memory loading
• Computation Scheduling: Task scheduling optimization
• Background Optimization: Asynchronous optimization tasks

Primary Use Cases:

• Optimizing system performance
• Reducing computational overhead
• Improving memory efficiency
• Enhancing system responsiveness

Services Module

The services module provides advanced retrieval and processing services:

Key Components:

• Semantic Matching: Content-based similarity services
• Contextual Scoring: Context-aware relevance assessment
• Multi-criteria Ranking: Sophisticated ranking systems
• Memory Hierarchy: Structured memory organization
• Retrieval Caching: Efficient caching services

Primary Use Cases:

• Implementing advanced retrieval strategies
• Building semantic matching systems
• Creating sophisticated ranking mechanisms
• Organizing memory hierarchically

Utils Module

The utils module provides infrastructure utilities and helper functions:

Key Components:

• Hook Manager: Comprehensive hook management
• Memory Hierarchy: Memory organization utilities
• Retrieval Cache: Caching utilities
• Helper Functions: Various utility functions
• Configuration Management: Configuration utilities

Primary Use Cases:

• Managing transformer hooks
• Organizing memory structures
• Implementing caching systems
• Providing utility functions

Getting Started

Quick Start Guide

1. Installation: Install required dependencies
2. Basic Setup: Initialize a basic CMR model
3. Configuration: Configure memory and retrieval settings
4. Usage: Process text with memory enhancement
5. Monitoring: Monitor performance and health

Basic Usage Example

from models.cmr_full_integrated import FullCMRModel
from transformers import AutoConfig, AutoTokenizer

# Create model configuration
# Note: Using remote configs (e.g., mistralai/...) requires network/HF access.
# For fully offline tests, pass a locally constructed config.
base_config = AutoConfig.from_pretrained("mistralai/Ministral-8B-Instruct-2410")

# Configure CMR settings
cmr_config = {
    'target_layers': [4, 8, 12],
    'max_entries_per_layer': 1000,
    'scoring_method': 'hybrid',
    'retrieval_strategy': 'multi_criteria'
}

# Initialize CMR model
model = FullCMRModel(base_config, cmr_config)

# Create tokenizer and process input with memory
tokenizer = AutoTokenizer.from_pretrained("mistralai/Ministral-8B-Instruct-2410")
input_ids = tokenizer.encode("Your input text here", return_tensors="pt")
outputs = model.forward(input_ids, return_memory_info=True)

# Access results and memory information
generated_states = outputs['last_hidden_state']
memory_stats = {k: outputs[k] for k in ("memory_stats", "performance_stats", "retrieval_stats") if k in outputs}

Note: The quickstart example uses Hugging Face model names and will require access. There is no automatic fallback to GPT‑2 in tokenization. FullCMRModel projects last_hidden_state to a 256‑dimensional hidden size by default for consistency in tests.

Configuration Guide

Basic Configuration

# Memory configuration
memory_config = {
    'target_layers': [4, 8, 12],          # Layers to capture from
    'max_entries_per_layer': 1000,        # Buffer size per layer
    'scoring_method': 'hybrid',           # Relevance scoring method
    'relevance_threshold': 0.5,           # Minimum relevance score
    'eviction_strategy': 'lru_relevance'  # Memory eviction strategy
}

# Retrieval configuration
retrieval_config = {
    'strategy': 'multi_criteria',         # Retrieval strategy
    'similarity_threshold': 0.7,          # Similarity threshold
    'context_heads': 8,                   # Contextual scorer heads
    'cache_size': 1000,                   # Retrieval cache size
    'max_clusters': 32,                   # Hierarchical clustering granularity
    'criteria_weights': {                 # Multi-criteria weights
        'relevance': 0.4,
        'similarity': 0.3,
        'recency': 0.2,
        'diversity': 0.1
    }
}

# Memory selection count is controlled at call time via RetrievalContext.retrieval_budget

# Optimization configuration
optimization_config = {
    'enable_adaptive_thresholds': True,   # Enable adaptive thresholds
    'enable_batch_optimization': True,    # Enable batch optimization
    'enable_prefetching': True,           # Enable memory prefetching
    'enable_background_optimization': True # Enable background tasks
}

Advanced Configuration

For advanced configuration options, refer to the individual module documentation. Note that IntegratedCMRModel is planned and not available; FullCMRModel defaults to attention_based relevance scoring.

Performance Considerations

Memory Management

• Buffer Sizing: Configure appropriate buffer sizes for your use case
• Eviction Strategies: Choose optimal eviction policies
• Memory Monitoring: Monitor memory usage continuously
• Cleanup Procedures: Implement regular cleanup routines

Computational Efficiency

• Batch Processing: Optimize batch sizes for throughput
• Caching: Implement effective caching strategies
• Prefetching: Use predictive loading for better performance
• Background Processing: Leverage asynchronous operations

Hardware Optimization

• GPU Utilization: Optimize for GPU acceleration
• Memory Allocation: Efficient memory allocation strategies
• Quantization: Use appropriate precision for your hardware
• Device Management: Optimize device placement and usage

Best Practices

Development

1. Modular Design: Use modular architecture principles
2. Configuration Management: Use comprehensive configuration systems
3. Error Handling: Implement robust error handling
4. Testing: Write comprehensive tests for all components

Deployment

1. Performance Monitoring: Implement comprehensive monitoring
2. Resource Management: Manage system resources effectively
3. Scaling: Design for horizontal and vertical scaling
4. Maintenance: Plan for system maintenance and updates

Optimization

1. Profiling: Profile your specific use case regularly
2. Tuning: Iteratively tune system parameters
3. Monitoring: Monitor optimization impact continuously
4. Validation: Validate performance improvements

Troubleshooting

Common Issues

• Memory Errors: Reduce buffer sizes or optimize memory usage
• Performance Issues: Enable optimization features and tune parameters
• Integration Problems: Check component compatibility and configuration
• Resource Conflicts: Monitor and manage resource usage

Getting Help

• Documentation: Refer to module-specific documentation
• Examples: Check demo scripts and usage examples
• Configuration: Validate configuration parameters
• Monitoring: Use monitoring tools to diagnose issues

Contributing

Development Guidelines

1. Code Style: Follow established code style guidelines
2. Documentation: Document all new features and changes
3. Testing: Write tests for new functionality
4. Performance: Consider performance impact of changes

Module Structure

When adding new functionality:

1. Choose the appropriate module for your changes
2. Follow existing patterns and conventions
3. Update documentation accordingly
4. Add appropriate tests and examples

Version Information

This documentation covers the current version of the CMR system. For version-specific information and changelog, refer to the project repository.

License

This project is licensed under the terms specified in the project repository.