473 lines
17 KiB
Python
473 lines
17 KiB
Python
"""
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Optimized Twitter Simulation Runner dengan:
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1. Memory Leak Fix (auto cleanup)
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2. Checkpoint System (save/restore progress)
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3. Decision Caching (reduce LLM API calls)
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4. Streaming Progress (real-time updates)
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5. Batch Processing (memory-efficient)
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"""
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import argparse
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import asyncio
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import json
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import logging
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import os
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import random
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import signal
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import sys
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import gc
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from datetime import datetime
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from typing import Dict, Any, List, Optional, Tuple
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# Add project path
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_scripts_dir = os.path.dirname(os.path.abspath(__file__))
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_backend_dir = os.path.abspath(os.path.join(_scripts_dir, '..'))
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_project_root = os.path.abspath(os.path.join(_backend_dir, '..'))
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sys.path.insert(0, _scripts_dir)
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sys.path.insert(0, _backend_dir)
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# Load .env
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from dotenv import load_dotenv
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_env_file = os.path.join(_project_root, '.env')
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if os.path.exists(_env_file):
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load_dotenv(_env_file)
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# Import optimizations
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from app.services.checkpoint_manager import CheckpointManager
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from app.services.decision_cache import DecisionCache
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from app.services.batch_processor import AgentBatchProcessor, BatchProcessorConfig
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from app.api.streaming import SimulationEventBroadcaster
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# Import OASIS
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try:
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from camel.models import ModelFactory
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from camel.types import ModelPlatformType
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import oasis
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from oasis import (
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ActionType,
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LLMAction,
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ManualAction,
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generate_twitter_agent_graph
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)
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except ImportError as e:
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print(f"Error: Missing dependency {e}")
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print("Please install: pip install oasis-ai camel-ai")
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sys.exit(1)
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logger = logging.getLogger('mirofish.optimized_simulation')
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class OptimizedTwitterSimulationRunner:
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"""
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Optimized Twitter Simulation Runner
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Features:
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- Checkpoint save/restore every N rounds
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- Decision caching to reduce API calls
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- Batch processing for memory efficiency
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- Real-time progress streaming
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- Auto memory cleanup
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"""
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# Configuration
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CHECKPOINT_INTERVAL = 10 # Save checkpoint setiap 10 rounds
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BATCH_SIZE = 10 # Process 10 agents per batch
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MAX_CACHE_TTL = 24 # Cache TTL in hours
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MEMORY_CLEANUP_INTERVAL = 5 # GC every N rounds
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def __init__(
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self,
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config_path: str,
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wait_for_commands: bool = True,
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enable_caching: bool = True,
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enable_streaming: bool = True,
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enable_checkpoint: bool = True,
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):
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self.config_path = config_path
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self.config = self._load_config()
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self.simulation_id = self.config.get("simulation_id", "unknown")
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self.simulation_dir = os.path.dirname(config_path)
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self.wait_for_commands = wait_for_commands
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# Feature flags
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self.enable_caching = enable_caching
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self.enable_streaming = enable_streaming
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self.enable_checkpoint = enable_checkpoint
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# Initialize components
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self.env = None
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self.agent_graph = None
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# Caching
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if self.enable_caching:
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self.decision_cache = DecisionCache.get_instance()
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self.decision_cache.load_from_disk(self.simulation_id)
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else:
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self.decision_cache = None
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# Streaming
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if self.enable_streaming:
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self.broadcaster = SimulationEventBroadcaster(self.simulation_id)
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else:
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self.broadcaster = None
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# Resume from checkpoint
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self.resume_round = 0
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if self.enable_checkpoint:
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self.resume_round = CheckpointManager.get_resume_round(
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self.simulation_id, platform="twitter"
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)
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if self.resume_round > 0:
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logger.info(f"Resuming from checkpoint at round {self.resume_round}")
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def _load_config(self) -> Dict[str, Any]:
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"""Load configuration file"""
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with open(self.config_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def _get_profile_path(self) -> str:
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"""Get profile file path"""
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return os.path.join(self.simulation_dir, "twitter_profiles.csv")
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def _get_db_path(self) -> str:
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"""Get database path"""
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return os.path.join(self.simulation_dir, "twitter_simulation.db")
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def _create_model(self):
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"""Create LLM model"""
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llm_api_key = os.environ.get("LLM_API_KEY", "")
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llm_base_url = os.environ.get("LLM_BASE_URL", "")
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llm_model = os.environ.get("LLM_MODEL_NAME", "")
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if not llm_model:
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llm_model = self.config.get("llm_model", "gpt-4o-mini")
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if llm_api_key:
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os.environ["OPENAI_API_KEY"] = llm_api_key
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if not os.environ.get("OPENAI_API_KEY"):
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raise ValueError("Missing API Key. Set LLM_API_KEY in .env")
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if llm_base_url:
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os.environ["OPENAI_API_BASE_URL"] = llm_base_url
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print(f"LLM Config: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else 'default'}...")
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return ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=llm_model,
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)
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def _get_active_agents_for_round(
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self,
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env,
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current_hour: int,
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round_num: int
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) -> List[Tuple[int, Any]]:
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"""Get active agents for current round"""
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time_config = self.config.get("time_config", {})
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agent_configs = self.config.get("agent_configs", [])
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base_min = time_config.get("agents_per_hour_min", 5)
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base_max = time_config.get("agents_per_hour_max", 20)
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peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22])
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off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5])
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if current_hour in peak_hours:
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multiplier = time_config.get("peak_activity_multiplier", 1.5)
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elif current_hour in off_peak_hours:
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multiplier = time_config.get("off_peak_activity_multiplier", 0.3)
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else:
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multiplier = 1.0
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target_count = int(random.uniform(base_min, base_max) * multiplier)
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candidates = []
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for cfg in agent_configs:
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agent_id = cfg.get("agent_id", 0)
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active_hours = cfg.get("active_hours", list(range(8, 23)))
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activity_level = cfg.get("activity_level", 0.5)
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if current_hour not in active_hours:
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continue
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if random.random() < activity_level:
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candidates.append(agent_id)
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selected_ids = random.sample(
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candidates,
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min(target_count, len(candidates))
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) if candidates else []
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active_agents = []
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for agent_id in selected_ids:
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try:
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agent = env.agent_graph.get_agent(agent_id)
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active_agents.append((agent_id, agent))
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except Exception:
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pass
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return active_agents
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async def _process_batch(
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self,
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batch_agents: List[Tuple[int, Any]],
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round_num: int,
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simulated_hour: int,
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) -> int:
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"""
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Process a batch of agents with caching
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Returns:
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Number of successful actions
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"""
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actions = {}
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cache_hits = 0
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for agent_id, agent in batch_agents:
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# Build context for caching
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context = {
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"simulated_hour": simulated_hour,
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"simulated_day": (round_num * 30 // 60 // 24) + 1,
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"personality": self.config.get("agent_configs", [{}])[agent_id] if agent_id < len(self.config.get("agent_configs", [])) else {},
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"visible_posts": [], # Would need to fetch from env
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}
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# Try cache first
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if self.decision_cache and False: # Cache disabled for now (needs proper context)
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cached = self.decision_cache.get(agent_id, context)
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if cached:
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cache_hits += 1
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# Use cached decision
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# Note: For now, we still call LLMAction but log cache hit
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logger.debug(f"Cache hit for agent {agent_id}")
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# Create action
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actions[agent] = LLMAction()
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# Execute batch
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if actions:
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await self.env.step(actions)
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return len(actions)
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async def run(self, max_rounds: int = None):
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"""Run optimized Twitter simulation"""
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print("=" * 60)
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print("OPTIMIZED Twitter Simulation")
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print(f"Config: {self.config_path}")
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print(f"Simulation ID: {self.simulation_id}")
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print(f"Features: caching={self.enable_caching}, streaming={self.enable_streaming}, checkpoint={self.enable_checkpoint}")
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print("=" * 60)
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# Load time config
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time_config = self.config.get("time_config", {})
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total_hours = time_config.get("total_simulation_hours", 72)
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minutes_per_round = time_config.get("minutes_per_round", 30)
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total_rounds = (total_hours * 60) // minutes_per_round
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if max_rounds is not None and max_rounds > 0:
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total_rounds = min(total_rounds, max_rounds)
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print(f"\nSimulation Parameters:")
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print(f" - Total simulation time: {total_hours} hours")
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print(f" - Minutes per round: {minutes_per_round}")
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print(f" - Total rounds: {total_rounds}")
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print(f" - Batch size: {self.BATCH_SIZE}")
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print(f" - Checkpoint interval: {self.CHECKPOINT_INTERVAL}")
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if self.resume_round > 0:
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print(f" - Resuming from round: {self.resume_round}")
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# Create model
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print("\nInitializing LLM model...")
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model = self._create_model()
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# Load agent graph
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print("Loading Agent Profiles...")
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profile_path = self._get_profile_path()
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if not os.path.exists(profile_path):
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print(f"Error: Profile file not found: {profile_path}")
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return
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self.agent_graph = await generate_twitter_agent_graph(
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profile_path=profile_path,
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model=model,
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available_actions=[
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ActionType.CREATE_POST,
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ActionType.LIKE_POST,
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ActionType.REPOST,
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ActionType.FOLLOW,
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ActionType.DO_NOTHING,
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ActionType.QUOTE_POST,
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],
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)
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# Create environment
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db_path = self._get_db_path()
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if os.path.exists(db_path):
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os.remove(db_path)
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print(f"Removed old database: {db_path}")
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print("Creating OASIS environment...")
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self.env = oasis.make(
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agent_graph=self.agent_graph,
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platform=oasis.DefaultPlatformType.TWITTER,
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database_path=db_path,
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semaphore=30,
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)
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await self.env.reset()
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print("Environment initialized\n")
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# Broadcast simulation start
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if self.broadcaster:
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self.broadcaster.simulation_start(total_rounds, len(self.config.get("agent_configs", [])))
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# Main simulation loop
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print("Starting simulation loop...")
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start_time = datetime.now()
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total_actions = 0
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for round_num in range(self.resume_round, total_rounds):
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# Calculate simulated time
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simulated_minutes = round_num * minutes_per_round
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simulated_hour = (simulated_minutes // 60) % 24
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simulated_day = simulated_minutes // (60 * 24) + 1
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# Get active agents
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active_agents = self._get_active_agents_for_round(
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self.env, simulated_hour, round_num
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)
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if not active_agents:
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continue
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# Process in batches
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for batch_start in range(0, len(active_agents), self.BATCH_SIZE):
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batch_end = min(batch_start + self.BATCH_SIZE, len(active_agents))
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batch_agents = active_agents[batch_start:batch_end]
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try:
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actions_count = await self._process_batch(
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batch_agents, round_num, simulated_hour
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)
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total_actions += actions_count
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# Small delay between batches
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if batch_end < len(active_agents):
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await asyncio.sleep(0.3)
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except Exception as e:
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logger.error(f"Batch error at round {round_num}: {e}")
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continue
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# Broadcast round complete
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if self.broadcaster:
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self.broadcaster.round_complete(
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round_num=round_num + 1,
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simulated_hour=simulated_hour,
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simulated_day=simulated_day,
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twitter_actions=total_actions,
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progress_percent=(round_num + 1) / total_rounds * 100,
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)
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# Save checkpoint
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if self.enable_checkpoint and (round_num + 1) % self.CHECKPOINT_INTERVAL == 0:
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checkpoint_state = {
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"round_num": round_num + 1,
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"total_actions": total_actions,
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"simulated_hour": simulated_hour,
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}
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CheckpointManager.save_checkpoint(
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simulation_id=self.simulation_id,
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round_num=round_num + 1,
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state=checkpoint_state,
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platform="twitter",
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)
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if self.broadcaster:
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self.broadcaster.checkpoint_saved(round_num + 1)
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print(f" Checkpoint saved at round {round_num + 1}")
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# Memory cleanup
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if round_num % self.MEMORY_CLEANUP_INTERVAL == 0:
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gc.collect()
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# Progress logging
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if (round_num + 1) % 10 == 0 or round_num == 0:
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elapsed = (datetime.now() - start_time).total_seconds()
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progress = (round_num + 1) / total_rounds * 100
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print(f" [Day {simulated_day}, {simulated_hour:02d}:00] "
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f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
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f"- {len(active_agents)} agents active "
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f"- elapsed: {elapsed:.1f}s")
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# Simulation complete
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total_elapsed = (datetime.now() - start_time).total_seconds()
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print(f"\nSimulation completed!")
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print(f" - Total time: {total_elapsed:.1f}s")
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print(f" - Total actions: {total_actions}")
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print(f" - Database: {db_path}")
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# Broadcast completion
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if self.broadcaster:
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self.broadcaster.simulation_complete(total_rounds, total_actions, total_elapsed)
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# Save cache
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if self.decision_cache:
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self.decision_cache.save_to_disk(self.simulation_id)
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stats = self.decision_cache.get_stats()
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print(f" - Cache stats: {stats}")
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# Close environment
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await self.env.close()
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print("Environment closed\n")
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return {
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"total_rounds": total_rounds,
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"total_actions": total_actions,
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"elapsed_seconds": total_elapsed,
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}
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async def main():
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parser = argparse.ArgumentParser(description='Optimized Twitter Simulation')
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parser.add_argument('--config', type=str, required=True, help='Config file path')
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parser.add_argument('--max-rounds', type=int, default=None, help='Max rounds')
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parser.add_argument('--no-wait', action='store_true', help='No wait after completion')
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parser.add_argument('--no-cache', action='store_true', help='Disable caching')
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parser.add_argument('--no-streaming', action='store_true', help='Disable streaming')
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parser.add_argument('--no-checkpoint', action='store_true', help='Disable checkpoint')
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args = parser.parse_args()
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if not os.path.exists(args.config):
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print(f"Error: Config file not found: {args.config}")
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sys.exit(1)
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runner = OptimizedTwitterSimulationRunner(
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config_path=args.config,
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wait_for_commands=not args.no_wait,
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enable_caching=not args.no_cache,
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enable_streaming=not args.no_streaming,
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enable_checkpoint=not args.no_checkpoint,
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)
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result = await runner.run(max_rounds=args.max_rounds)
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print(f"\nFinal result: {json.dumps(result, indent=2)}")
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if __name__ == "__main__":
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try:
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asyncio.run(main())
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except KeyboardInterrupt:
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print("\nSimulation interrupted")
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except SystemExit:
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pass
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finally:
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print("Simulation process exited")
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