Update memory_models.py #37
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Neural_memory.py changes1. Advanced Neural Memory HierarchyThe system uses a 12-tier memory organization that goes far beyond the typical attention window: FOCUS/ACTIVE/FOREGROUND: Ultra-high-precision current working memory BACKGROUND/EPISODIC: Recent context with high fidelity SEMANTIC/GENERAL: Distilled knowledge representations CATEGORICAL/ARCHIVAL/REFERENCE: Highly compressed long-term storage CONSOLIDATED/OFFLOADED: Neural-symbolic representations for extreme distances 2. Neural-Symbolic CompressionRather than simple truncation or fixed-window attention, the system uses: Progressive multi-resolution compression based on token distance and importance Vector quantization with specialized codebooks for different token types Semantic distillation that captures meaning while discarding surface form Ultra-low bit precision scaling (down to 1-bit) for distant tokens 3. Cognitive Attention MechanismsInspired by human cognition patterns: Hierarchical Transformer Attention: Multi-scale processing with dedicated pathways Recurrent Memory Mechanisms: Compresses global context into reusable state Multi-hop Reasoning: Allows traversing connections between distant tokens Global Token Integration: Strategically indexed tokens provide navigation points 4. Advanced Efficiency TechniquesBlock-sparse FFN: Extreme parameter efficiency with structured sparsity 5. Automatic Memory ManagementCognitive Signal Processing: Tracks token importance using 10 cognitive signals Dynamic Tier Allocation: Auto-scales memory tiers based on usage patterns Memory Consolidation: Extracts and stores semantic information from token groups Intelligent Token Pruning: Removes redundant tokens while preserving information This system completely redefines what's possible in large context AI models, creating a foundation for true long-term memory and reasoning across millions of tokens of context. Practical ApplicationsThis architecture enables entirely new AI capabilities: Processing entire codebases (100M+ tokens) simultaneously Analyzing years of financial or scientific data in a single context Maintaining coherent conversation history over thousands of interactions True long-form content generation with global coherence Book-length document understanding with reference to arbitrary sections |
…IntegrationAPI.py
…xtIntegrationModule.py.py
UltraContext System# UltraContext: Enterprise Technical Analysis of 100M Token Context Window System
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## 1. Introduction to UltraContext Architecture
UltraContext is an advanced system designed to **extend language model context windows to 100 million tokens and beyond**. The framework introduces a multi-level architecture with specialized components for memory management, attention mechanisms, token compression, and dynamic processing.
At its core, UltraContext employs a hierarchical approach to handle extremely long contexts by strategically managing how information is stored, accessed, compressed, and processed throughout the system, effectively addressing the computational and memory limitations of traditional attention mechanisms.
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## 2. Core Technical Components
### 2.1 Memory Hierarchy System
UltraContext implements a **multi-tiered memory system** similar to modern CPU cache hierarchies:
┌──────────────────────────────────────────────────┐ Key attention technologies include:
2.3 Token Compression SystemThe class ContextualCompressor(Module):
"""
Contextually-aware token compression for ultra-long contexts
Features:
- Content-based token importance estimation
- Dynamic compression rates based on token importance
- Multiple compression strategies (pruning, merging, summarizing)
- Preserves crucial information while reducing context size
"""Compression strategies include:
3. Processing Architecture3.1 Hierarchical Processing ModuleThe This approach processes information at multiple granularities, with:
3.2 Token Streaming SystemFor real-time generation, the class TokenStreamProcessor(Module):
"""
Process token streams efficiently for real-time generation
Features:
- Efficient handling of streamed tokens
- Adaptive window management
- Compressed history representation
- Low-latency inference optimizations
"""Key streaming capabilities:
4. Memory Management Technologies4.1 Importance Scoring and Token PrioritizationUltraContext employs sophisticated algorithms to determine token importance: class ImportanceScorer(Module):
"""
Scores token importance based on multiple factors:
- Attention weights from the model
- Access patterns
- Semantic relevance to queries
- Position in sequence
- Token rarity/information content
"""This enables:
4.2 Advanced Memory OperationsThe memory system implements enterprise-grade features: class AdaptiveMemoryPolicy(Module):
"""
Adaptively tunes memory management policies based on:
- Observed access patterns
- Hardware resources
- Priority workloads
- Real-time performance metrics
"""Features include:
5. Integration Capabilities5.1 Model IntegrationUltraContext provides seamless integration with existing models through the class ModelIntegrator:
"""
Utility for integrating UltraContext with various model architectures
"""Integration methods include:
The system can automatically detect model architecture and apply appropriate integration strategies for Hugging Face transformers, PyTorch models, and other frameworks. 5.2 API Interfaceclass UltraContext:
"""
Unified API for UltraContext
This class provides a simple, unified interface for using UltraContext
with any model, managing the context window, memory, and integration.
"""The API supports:
6. Performance Optimizations6.1 Computational Efficiency@dataclass
class PerformanceConfig:
"""Advanced configuration for performance optimizations"""
# Precision options
use_mixed_precision: bool = True
default_dtype: torch.dtype = torch.float16
compute_dtype: torch.dtype = torch.float32
# Acceleration options
use_xformers: bool = XFORMERS_AVAILABLE
use_flash_attention: bool = FLASH_ATTN_AVAILABLE
use_triton: bool = TRITON_AVAILABLE
use_torch_compile: bool = TORCH_COMPILE_AVAILABLEThe system implements:
6.2 Memory EfficiencyAdvanced memory techniques include:
7. Technical Implementation Details7.1 Position Encodings for Ultra-Long ContextsUltraContext supports specialized position encoding mechanisms: # Position encoding strategies
self.config.position_encoding = "adaptive" # "absolute", "relative", "rotary", "adaptive"These encodings are designed to work effectively beyond traditional position limits:
7.2 Quality of Service GuaranteesThe system implements QoS tracking and guarantees: # QoS targets
self.qos_targets = qos_targets or {
"l1_latency_ms": 0.1,
"l2_latency_ms": 1.0,
"l3_latency_ms": 10.0,
"disk_latency_ms": 100.0,
"hit_rate_l1": 0.9,
"hit_rate_l2": 0.8,
"hit_rate_l3": 0.7,
"availability": 0.9999,
}This enables enterprise-grade reliability and performance through:
7.3 Distributed Memory ArchitectureFor massive context windows, UltraContext provides distributed memory orchestration: class DistributedMemoryOrchestrator:
"""
Production-ready distributed memory coordination system
for extreme-scale context windows across multiple nodes
"""The distributed system features:
8. Retrieval AugmentationUltraContext implements retrieval augmentation for enhanced context processing: class RetrievalAugmentedProcessor(Module):
"""
Enhances context with retrieval from external knowledge
Features:
- Query generation from current context
- Retrieval from large token stores
- Integration of retrieved information
- Attention-based weighting of retrieved content
"""This enables:
9. Technical Performance CharacteristicsThe system's performance scales with context length through:
10. ConclusionUltraContext represents a comprehensive approach to enabling 100M+ token context windows through its hierarchical architecture, advanced memory systems, specialized attention mechanisms, and adaptive processing strategies. By implementing a sophisticated system that intelligently manages, compresses, and processes information across multiple tiers, it overcomes the computational and memory limitations that typically constrain context length in language models. The system's modular design, integration capabilities, and performance optimizations make it suitable for enterprise deployments requiring ultra-long context processing at scale. |
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Going to update Ultra Context with everything missing from the neural memory file tomorrow |
New High-Performance Features
Notes:
Interface Changes:
The optimized version adds new parameters to most class initializers (perf_config, dropout, bias, etc.)
These new parameters have default values, but they change the function signatures
Behavioral Changes:
The optimized version supports pre-norm architecture (original only had post-norm)
Implementation of some functions like l2norm has been modified with added parameters
Added Functionality:
New gradient checkpointing features
Optional mixed precision support
Memory optimizations
Optional torch.compile integration
Compatibility Analysis by Component
LayerNorm:
Not drop-in compatible due to new parameters
Original: LayerNorm(dim)
Optimized: LayerNorm(dim, elementwise_affine=False, eps=1e-5, bias=True, device=None, dtype=None, perf_config=DEFAULT_PERF_CONFIG)
ResidualNorm:
Not drop-in compatible due to additional parameters
Original: ResidualNorm(dim, model)
Optimized: ResidualNorm(dim, model, pre_norm=False, dropout=0.0, perf_config=DEFAULT_PERF_CONFIG)
Behavior change: Added pre_norm option
MemoryMLP:
Not drop-in compatible due to new parameters
Added regularization and optimization features
GatedResidualMemoryMLP, FactorizedMemoryMLP, MemorySwiGluMLP:
Not drop-in compatible due to additional parameters
MemoryAttention:
Not drop-in compatible due to multiple new parameters
Added multi-head attention support and different attention implementations
New Factory Function:
create_optimized_memory_network: No equivalent in original code
Making It Compatible
If drop-in backwards compatibility is desired, create wrapper functions that match the original signatures and internally call the optimized versions with default configurations