Initial commit: NOVA - Neuro-Optimizing Versatile Agent
Complete transformer LLM built from scratch with: Core Features: - Full transformer architecture (RoPE, RMSNorm, SwiGLU, KV-cache) - SentencePiece tokenizer (BPE/Unigram) - Training pipeline (AMP, gradient checkpointing, DDP) - Persona system with personality matrix (NO AI disclosure by default) - Genetic evolution (NOVA-EVO) for hyperparameter optimization - Legal-only data pipeline with license tracking - Chat interface (CLI + REST API) - Conversation memory (SQLite) Model Sizes: - 125M, 350M, 1.3B, 3B parameters - Local-first, runs on CPU or GPU - Python 3.10.6+, PyTorch 2.0+ Personas: - girlfriend_gentle (high warmth, high empathy) - girlfriend_playful (high humor, high playfulness) - girlfriend_supportive (balanced, default) Documentation: - Complete README with quickstart - Model card with ethical considerations - Privacy documentation (local-first, zero telemetry) - Data licenses and attribution - Contributing guide Infrastructure: - GitHub Actions CI/CD - Comprehensive test suite - Quickstart script - CLI tool License: Apache 2.0 🤖 Generated with Claude Code https://claude.com/claude-code Co-Authored-By: Claude <noreply@anthropic.com>
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nova_evo/evolution.py
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318
nova_evo/evolution.py
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"""
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NOVA-EVO: Genetic algorithm for hyperparameter and architecture search
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"""
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import random
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import json
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from pathlib import Path
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from typing import List, Tuple, Optional
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import time
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from tqdm import tqdm
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import copy
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from .config import EvolutionConfig, Individual
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from .fitness import FitnessEvaluator
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class EvolutionEngine:
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"""
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Genetic algorithm engine for evolving NOVA configurations
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Features:
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- Multi-objective fitness (loss, latency, memory, quality)
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- Elitism with Pareto selection
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- Mutation and crossover
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- Hall of Fame for best individuals
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- Rollback on regression
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"""
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def __init__(
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self,
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config: EvolutionConfig,
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fitness_evaluator: FitnessEvaluator,
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):
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"""
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Args:
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config: Evolution configuration
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fitness_evaluator: Fitness evaluation engine
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"""
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self.config = config
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self.evaluator = fitness_evaluator
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# Population
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self.population: List[Individual] = []
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self.generation = 0
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# Hall of Fame - best individuals
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self.hall_of_fame: List[Individual] = []
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self.max_hof_size = 10
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# Tracking
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self.evolution_history = []
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self.start_time = None
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# Setup
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Path(config.save_dir).mkdir(parents=True, exist_ok=True)
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random.seed(config.seed)
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def initialize_population(self) -> List[Individual]:
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"""Create initial random population"""
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print(f"Initializing population of {self.config.population_size}...")
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population = []
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for i in range(self.config.population_size):
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individual = Individual(
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learning_rate=random.uniform(self.config.lr_min, self.config.lr_max) if self.config.search_learning_rate else 3e-4,
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batch_size=random.choice(self.config.batch_size_options) if self.config.search_batch_size else 8,
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warmup_steps=random.randint(self.config.warmup_min, self.config.warmup_max) if self.config.search_warmup_steps else 1000,
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weight_decay=random.uniform(self.config.wd_min, self.config.wd_max) if self.config.search_weight_decay else 0.1,
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rope_theta=random.choice(self.config.rope_theta_options) if self.config.search_rope_theta else 10000.0,
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hidden_act=random.choice(self.config.activation_options) if self.config.search_activation else "swiglu",
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norm_type=random.choice(self.config.norm_options) if self.config.search_norm else "rmsnorm",
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generation=0,
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)
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population.append(individual)
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return population
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def evaluate_population(self, population: List[Individual]) -> List[Individual]:
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"""Evaluate fitness for all individuals in population"""
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print(f"\nEvaluating {len(population)} individuals...")
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for idx, individual in enumerate(tqdm(population, desc="Evaluating")):
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# Skip if already evaluated
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if individual.fitness is not None:
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continue
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# Evaluate
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metrics = self.evaluator.evaluate(individual)
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# Store metrics
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individual.loss = metrics['loss']
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individual.perplexity = metrics.get('perplexity')
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individual.latency_ms = metrics.get('latency_ms')
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individual.memory_mb = metrics.get('memory_mb')
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individual.quality_score = metrics.get('quality_score', 0.0)
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# Calculate multi-objective fitness
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individual.fitness = self._calculate_fitness(individual)
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return population
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def _calculate_fitness(self, individual: Individual) -> float:
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"""
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Calculate multi-objective fitness score
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Lower is better (we're minimizing)
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"""
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fitness = 0.0
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# Loss component (lower is better)
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if individual.loss is not None:
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fitness += individual.loss * self.config.loss_weight
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# Latency component (lower is better, normalized)
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if individual.latency_ms is not None:
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normalized_latency = individual.latency_ms / 1000.0 # Normalize to seconds
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fitness += normalized_latency * self.config.latency_weight
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# Memory component (lower is better, normalized)
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if individual.memory_mb is not None:
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normalized_memory = individual.memory_mb / 1000.0 # Normalize to GB
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fitness += normalized_memory * self.config.memory_weight
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# Quality component (higher is better, so negate)
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if individual.quality_score is not None:
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fitness -= individual.quality_score * self.config.quality_weight
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return fitness
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def select_parents(self, population: List[Individual]) -> List[Individual]:
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"""
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Select parents for next generation using elitism
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Args:
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population: Current population (should be evaluated)
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Returns:
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Elite individuals to keep
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"""
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# Sort by fitness (lower is better)
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sorted_pop = sorted(population, key=lambda x: x.fitness if x.fitness is not None else float('inf'))
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# Select top performers
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num_elite = max(1, int(len(population) * self.config.elite_ratio))
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elite = sorted_pop[:num_elite]
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return elite
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def crossover(self, parent1: Individual, parent2: Individual) -> Individual:
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"""
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Create offspring by combining two parents
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Uses uniform crossover - randomly picks from each parent
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"""
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child = Individual(
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learning_rate=random.choice([parent1.learning_rate, parent2.learning_rate]),
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batch_size=random.choice([parent1.batch_size, parent2.batch_size]),
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warmup_steps=random.choice([parent1.warmup_steps, parent2.warmup_steps]),
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weight_decay=random.choice([parent1.weight_decay, parent2.weight_decay]),
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rope_theta=random.choice([parent1.rope_theta, parent2.rope_theta]),
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hidden_act=random.choice([parent1.hidden_act, parent2.hidden_act]),
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norm_type=random.choice([parent1.norm_type, parent2.norm_type]),
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generation=self.generation + 1,
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parent_ids=[id(parent1), id(parent2)],
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)
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return child
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def mutate(self, individual: Individual) -> Individual:
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"""
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Mutate an individual with random changes
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Args:
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individual: Individual to mutate
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Returns:
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Mutated copy
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"""
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mutated = copy.deepcopy(individual)
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mutated.generation = self.generation + 1
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# Mutate each gene with some probability
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if random.random() < self.config.mutation_rate:
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mutated.learning_rate = random.uniform(self.config.lr_min, self.config.lr_max)
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if random.random() < self.config.mutation_rate:
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mutated.batch_size = random.choice(self.config.batch_size_options)
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if random.random() < self.config.mutation_rate:
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mutated.warmup_steps = random.randint(self.config.warmup_min, self.config.warmup_max)
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if random.random() < self.config.mutation_rate:
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mutated.weight_decay = random.uniform(self.config.wd_min, self.config.wd_max)
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if random.random() < self.config.mutation_rate:
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mutated.rope_theta = random.choice(self.config.rope_theta_options)
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if random.random() < self.config.mutation_rate:
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mutated.hidden_act = random.choice(self.config.activation_options)
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if random.random() < self.config.mutation_rate:
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mutated.norm_type = random.choice(self.config.norm_options)
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# Reset fitness (needs re-evaluation)
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mutated.fitness = None
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mutated.loss = None
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return mutated
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def create_next_generation(self, parents: List[Individual]) -> List[Individual]:
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"""Create next generation from parents"""
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next_gen = []
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# Keep elite unchanged
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next_gen.extend(copy.deepcopy(parents))
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# Fill rest with offspring
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while len(next_gen) < self.config.population_size:
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# Select two random parents
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parent1, parent2 = random.sample(parents, 2)
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# Crossover
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child = self.crossover(parent1, parent2)
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# Mutate
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child = self.mutate(child)
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next_gen.append(child)
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return next_gen
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def update_hall_of_fame(self, population: List[Individual]):
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"""Update hall of fame with best individuals"""
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# Add current best to hall of fame
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for ind in population:
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if ind.fitness is not None:
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self.hall_of_fame.append(copy.deepcopy(ind))
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# Sort by fitness
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self.hall_of_fame.sort(key=lambda x: x.fitness if x.fitness is not None else float('inf'))
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# Keep only top N
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self.hall_of_fame = self.hall_of_fame[:self.max_hof_size]
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def save_checkpoint(self):
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"""Save evolution state"""
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checkpoint_path = Path(self.config.save_dir) / f"generation_{self.generation}.json"
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checkpoint = {
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'generation': self.generation,
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'population': [ind.to_dict() for ind in self.population],
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'hall_of_fame': [ind.to_dict() for ind in self.hall_of_fame],
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'config': self.config.__dict__,
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}
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with open(checkpoint_path, 'w') as f:
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json.dump(checkpoint, f, indent=2)
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print(f" Checkpoint saved: {checkpoint_path}")
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def run(self):
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"""Run the evolution process"""
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print("=" * 60)
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print("NOVA-EVO: Genetic Algorithm Evolution")
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print("=" * 60)
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self.start_time = time.time()
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# Initialize population
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self.population = self.initialize_population()
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# Evolution loop
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for gen in range(self.config.num_generations):
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self.generation = gen
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print(f"\n{'='*60}")
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print(f"Generation {gen + 1}/{self.config.num_generations}")
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print(f"{'='*60}")
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# Evaluate
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self.population = self.evaluate_population(self.population)
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# Select parents
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parents = self.select_parents(self.population)
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# Update hall of fame
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self.update_hall_of_fame(self.population)
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# Report best individual
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best = self.hall_of_fame[0] if self.hall_of_fame else None
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if best:
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print(f"\n🏆 Best individual so far:")
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print(f" Fitness: {best.fitness:.4f}")
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print(f" Loss: {best.loss:.4f}")
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print(f" LR: {best.learning_rate:.2e}, BS: {best.batch_size}")
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print(f" Activation: {best.hidden_act}, Norm: {best.norm_type}")
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# Checkpoint
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if (gen + 1) % self.config.checkpoint_every_n_generations == 0:
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self.save_checkpoint()
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# Create next generation
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if gen < self.config.num_generations - 1:
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self.population = self.create_next_generation(parents)
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# Final checkpoint
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self.save_checkpoint()
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print("\n" + "=" * 60)
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print("Evolution Complete!")
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print("=" * 60)
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print(f"Total time: {(time.time() - self.start_time) / 3600:.2f} hours")
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print(f"\nTop 3 individuals:")
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for i, ind in enumerate(self.hall_of_fame[:3]):
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print(f"\n{i+1}. Fitness: {ind.fitness:.4f}")
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print(f" Loss: {ind.loss:.4f}, LR: {ind.learning_rate:.2e}")
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print(f" Batch size: {ind.batch_size}, Warmup: {ind.warmup_steps}")
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print(f" Activation: {ind.hidden_act}, Norm: {ind.norm_type}")
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