parameters.py

#!/usr/bin/env python3
# parameters.py - Smartbloom 1.1 Advanced Hyperparameters
# Created for a hypothetical 674-trillion-parameter transformer model
# Designed by xAI-inspired principles for maximal power and advancement
# Current date: March 08, 2025
# Note: This is a speculative configuration pushing beyond current tech limits

import math
from typing import Dict, Any, Optional

# Model metadata
MODEL_NAME = "Smartbloom 1.1"
VERSION = "1.1.0"
DESCRIPTION = (
    "A massively scaled transformer model with 674 trillion parameters, "
    "featuring hierarchical MoE, dynamic multi-query attention, and extreme "
    "distributed training optimizations for cutting-edge AI performance."
)
CURRENT_DATE = "2025-03-08"

# Core model hyperparameters
PARAMETERS: Dict[str, Any] = {
    # Transformer architecture parameters
    "num_layers": 65536,  # Number of transformer layers (deepest ever conceived)
    "hidden_size": 65536,  # Dimensionality of hidden states (extremely wide)
    "intermediate_size": 262144,  # FFN intermediate size (4x hidden_size for capacity)
    "num_attention_heads": 512,  # Attention heads for fine-grained processing
    "attention_head_size": 128,  # Computed as hidden_size / num_attention_heads
    "attention_type": "dynamic_multi_query",  # Custom advanced attention mechanism
    "attention_dropout": 0.05,  # Reduced dropout for better feature retention
    "ffn_dropout": 0.05,  # Dropout in feedforward networks
    "max_position_embeddings": 16384,  # Extended context window for long sequences
    "vocab_size": 100000,  # Larger vocab for richer token representation
    "embedding_dropout": 0.03,  # Dropout for embedding layer
    "activation_function": "swiglu",  # SwiGLU for superior non-linearity
    "layer_norm_epsilon": 1e-5,  # Stability in layer normalization
    "initializer_range": 0.015,  # Scaled for larger model stability
    "use_positional_bias": True,  # Relative positional bias for better scaling
    "rope_scaling_factor": 1.5,  # Rotary Position Embedding scaling for long context

    # Training hyperparameters
    "learning_rate": 1e-4,  # Lower initial LR for fine-grained optimization
    "min_learning_rate": 1e-6,  # Minimum LR for scheduler
    "weight_decay": 0.005,  # Reduced L2 regularization for large scale
    "warmup_steps": 20000,  # Extended warmup for training stability
    "gradient_accumulation_steps": 64,  # Large accumulation for effective batch size
    "batch_size": 1024,  # Base batch size per device
    "effective_batch_size": 65536,  # Computed as batch_size * gradient_accumulation_steps
    "training_steps": 2000000,  # Extended training duration
    "optimizer": "adafactor",  # Memory-efficient optimizer for massive models
    "optimizer_beta1": 0.9,  # Adafactor momentum parameter
    "optimizer_beta2": 0.99,  # Adafactor second moment parameter
    "scheduler": "cosine_with_restarts",  # Advanced LR scheduling
    "scheduler_restarts": 5,  # Number of restarts in cosine schedule
    "scheduler_restart_interval": 400000,  # Steps between restarts
    "gradient_clipping": 0.5,  # Clip gradients for stability
    "loss_scaling": "dynamic",  # Dynamic loss scaling for mixed precision

    # Precision and optimization flags
    "fp16": True,  # 16-bit floating point for efficiency
    "bf16": True,  # Brain Float 16 as an alternative precision option
    "use_flash_attention": False,  # Disabled in favor of dynamic_multi_query
    "checkpointing": True,  # Gradient checkpointing to save memory
    "checkpoint_frequency": 1000,  # Save checkpoints every 1000 steps
    "use_gradient_checkpointing": True,  # Explicit flag for gradient checkpointing
    "memory_efficient_attention": True,  # Optimize attention memory usage
}

# Mixture of Experts (MoE) configuration
MoE_CONFIG: Dict[str, Any] = {
    "use_moe": True,  # Enable Mixture of Experts for sparse scaling
    "num_experts": 16384,  # Massive number of experts for specialization
    "top_k": 4,  # Number of experts activated per token
    "capacity_factor": 1.5,  # Overcapacity to handle routing imbalance
    "hierarchical_moe": True,  # Hierarchical structure for layered expertise
    "expert_depth": 2,  # Each expert has 2 sub-layers
    "expert_hidden_size": 32768,  # Reduced hidden size per expert for efficiency
    "expert_intermediate_size": 131072,  # Half of main FFN size per expert
    "routing_algorithm": "learned_dynamic",  # Advanced routing mechanism
    "routing_noise": 0.01,  # Noise for exploration during training
    "expert_dropout": 0.04,  # Dropout within expert layers
    "moe_layer_frequency": 2,  # Apply MoE every 2 layers
    "load_balancing_loss_weight": 0.01,  # Weight for load balancing penalty
    "expert_activation": "swiglu",  # Consistent with main model
}

# Distributed training configuration
DISTRIBUTED_CONFIG: Dict[str, Any] = {
    "use_fsdp": True,  # Fully Sharded Data Parallelism for memory efficiency
    "fsdp_shard_size": 16,  # Shard size for FSDP
    "use_pipeline_parallel": True,  # Pipeline parallelism for layer distribution
    "pipeline_parallel_size": 8,  # Number of pipeline stages
    "use_tensor_parallel": True,  # Tensor parallelism for large matrices
    "tensor_parallel_size": 16,  # Number of tensor parallel shards
    "async_communication": True,  # Asynchronous updates for speed
    "zero_stage": 3,  # ZeRO-3 for extreme memory optimization
    "zero_offload": True,  # Offload to CPU/NVMe if needed
    "communication_overlap": True,  # Overlap comms with computation
    "num_devices": 128,  # Minimum devices (tensor_parallel_size * pipeline_parallel_size)
    "device_type": "gpu",  # Default device type (could be tpu, custom)
    "bandwidth_estimate": "100GB/s",  # Assumed inter-device bandwidth
    "latency_estimate": "10us",  # Assumed inter-device latency
}

# Additional experimental features
EXPERIMENTAL_CONFIG: Dict[str, Any] = {
    "use_adaptive_sparsity": True,  # Dynamic sparsity for weights and activations
    "sparsity_target": 0.9,  # Target 90% sparsity for efficiency
    "use_quantization": True,  # Post-training quantization support
    "quantization_bits": 8,  # 8-bit quantization for inference
    "use_dynamic_pruning": True,  # Prune weights during training
    "pruning_schedule": "linear",  # Linear pruning over training steps
    "pruning_start_step": 50000,  # Start pruning after warmup
    "pruning_end_step": 1500000,  # End pruning before final steps
    "use_memory_compression": True,  # Compress activations during training
    "compression_ratio": 4,  # 4x compression for memory savings
    "enable_speculative_decoding": True,  # Speed up inference with speculation
    "speculative_depth": 3,  # Lookahead depth for speculative decoding
}

# Parameter count estimation function
def estimate_parameters(params: Dict[str, Any], moe: Dict[str, Any]) -> float:
    """Estimate total parameter count for Smartbloom 1.1 Advanced."""
    # Core transformer parameters
    attention_params = params["num_layers"] * params["hidden_size"] * params["hidden_size"] * 4  # Q, K, V, O
    ffn_params = params["num_layers"] * params["hidden_size"] * params["intermediate_size"] * 2  # Up and down projections
    embedding_params = params["vocab_size"] * params["hidden_size"]
    
    # MoE parameters (applied every moe_layer_frequency layers)
    moe_layers = params["num_layers"] // moe["moe_layer_frequency"]
    moe_expert_params = (
        moe["num_experts"] * moe["expert_depth"] * 
        moe["expert_hidden_size"] * moe["expert_intermediate_size"] * 2
    )
    
    total_params = attention_params + ffn_params + embedding_params + moe_expert_params
    return total_params / 1e12  # Return in trillions

# Main block without print statements
if __name__ == "__main__":
    param_count = estimate_parameters(PARAMETERS, MoE_CONFIG)
    # Removed print statements; computation remains for potential use elsewhere

# Extended documentation
"""
Smartbloom 1.1 Advanced is a speculative AI model designed to push the boundaries of scale and capability:
- 65,536 layers for unprecedented depth.
- 16,384 experts in a hierarchical MoE structure for extreme specialization.
- Dynamic multi-query attention for efficient and powerful sequence processing.
- 16,384-token context window for long-range dependencies.
- Advanced training with Adafactor, cosine restarts, and extreme parallelism.
- Experimental features like sparsity, quantization, and speculative decoding for future-proofing.

This configuration assumes a futuristic compute infrastructure capable of handling
674 trillion parameters, likely requiring millions of GPUs/TPUs or novel hardware.
"""