Files
ComfyUI-Lora-Manager/py/utils/usage_stats.py
Will Miao 74507cef05 feat(settings): add validation for settings.json to ensure required configuration is present
fix(usage_stats): handle initialization errors for usage statistics when no valid paths are configured, fixes #375
2025-09-06 17:39:51 +08:00

378 lines
16 KiB
Python

import os
import json
import sys
import time
import asyncio
import logging
import datetime
import shutil
from typing import Dict, Set
from ..config import config
from ..services.service_registry import ServiceRegistry
# Check if running in standalone mode
standalone_mode = 'nodes' not in sys.modules
if not standalone_mode:
from ..metadata_collector.metadata_registry import MetadataRegistry
from ..metadata_collector.constants import MODELS, LORAS
logger = logging.getLogger(__name__)
class UsageStats:
"""Track usage statistics for models and save to JSON"""
_instance = None
_lock = asyncio.Lock() # For thread safety
# Default stats file name
STATS_FILENAME = "lora_manager_stats.json"
BACKUP_SUFFIX = ".backup"
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
# Initialize stats storage
self.stats = {
"checkpoints": {}, # sha256 -> { total: count, history: { date: count } }
"loras": {}, # sha256 -> { total: count, history: { date: count } }
"total_executions": 0,
"last_save_time": 0
}
# Queue for prompt_ids to process
self.pending_prompt_ids = set()
# Load existing stats if available
self._stats_file_path = self._get_stats_file_path()
self._load_stats()
# Save interval in seconds
self.save_interval = 90 # 1.5 minutes
# Start background task to process queued prompt_ids
self._bg_task = asyncio.create_task(self._background_processor())
self._initialized = True
logger.info("Usage statistics tracker initialized")
def _get_stats_file_path(self) -> str:
"""Get the path to the stats JSON file"""
if not config.loras_roots or len(config.loras_roots) == 0:
# If no lora roots are available, we can't save stats
# This will be handled by the caller
raise RuntimeError("No LoRA root directories configured. Cannot initialize usage statistics.")
# Use the first lora root
return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
def _backup_old_stats(self):
"""Backup the old stats file before conversion"""
if os.path.exists(self._stats_file_path):
backup_path = f"{self._stats_file_path}{self.BACKUP_SUFFIX}"
try:
shutil.copy2(self._stats_file_path, backup_path)
logger.info(f"Backed up old stats file to {backup_path}")
return True
except Exception as e:
logger.error(f"Failed to backup stats file: {e}")
return False
def _convert_old_format(self, old_stats):
"""Convert old stats format to new format with history"""
new_stats = {
"checkpoints": {},
"loras": {},
"total_executions": old_stats.get("total_executions", 0),
"last_save_time": old_stats.get("last_save_time", time.time())
}
# Get today's date in YYYY-MM-DD format
today = datetime.datetime.now().strftime("%Y-%m-%d")
# Convert checkpoint stats
if "checkpoints" in old_stats and isinstance(old_stats["checkpoints"], dict):
for hash_id, count in old_stats["checkpoints"].items():
new_stats["checkpoints"][hash_id] = {
"total": count,
"history": {
today: count
}
}
# Convert lora stats
if "loras" in old_stats and isinstance(old_stats["loras"], dict):
for hash_id, count in old_stats["loras"].items():
new_stats["loras"][hash_id] = {
"total": count,
"history": {
today: count
}
}
logger.info("Successfully converted stats from old format to new format with history")
return new_stats
def _is_old_format(self, stats):
"""Check if the stats are in the old format (direct count values)"""
# Check if any lora or checkpoint entry is a direct number instead of an object
if "loras" in stats and isinstance(stats["loras"], dict):
for hash_id, data in stats["loras"].items():
if isinstance(data, (int, float)):
return True
if "checkpoints" in stats and isinstance(stats["checkpoints"], dict):
for hash_id, data in stats["checkpoints"].items():
if isinstance(data, (int, float)):
return True
return False
def _load_stats(self):
"""Load existing statistics from file"""
try:
if os.path.exists(self._stats_file_path):
with open(self._stats_file_path, 'r', encoding='utf-8') as f:
loaded_stats = json.load(f)
# Check if old format and needs conversion
if self._is_old_format(loaded_stats):
logger.info("Detected old stats format, performing conversion")
self._backup_old_stats()
self.stats = self._convert_old_format(loaded_stats)
else:
# Update our stats with loaded data (already in new format)
if isinstance(loaded_stats, dict):
# Update individual sections to maintain structure
if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
self.stats["checkpoints"] = loaded_stats["checkpoints"]
if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
self.stats["loras"] = loaded_stats["loras"]
if "total_executions" in loaded_stats:
self.stats["total_executions"] = loaded_stats["total_executions"]
if "last_save_time" in loaded_stats:
self.stats["last_save_time"] = loaded_stats["last_save_time"]
logger.info(f"Loaded usage statistics from {self._stats_file_path}")
except Exception as e:
logger.error(f"Error loading usage statistics: {e}")
async def save_stats(self, force=False):
"""Save statistics to file"""
try:
# Only save if it's been at least save_interval since last save or force is True
current_time = time.time()
if not force and (current_time - self.stats.get("last_save_time", 0)) < self.save_interval:
return False
# Use a lock to prevent concurrent writes
async with self._lock:
# Update last save time
self.stats["last_save_time"] = current_time
# Create directory if it doesn't exist
os.makedirs(os.path.dirname(self._stats_file_path), exist_ok=True)
# Write to a temporary file first, then move it to avoid corruption
temp_path = f"{self._stats_file_path}.tmp"
with open(temp_path, 'w', encoding='utf-8') as f:
json.dump(self.stats, f, indent=2, ensure_ascii=False)
# Replace the old file with the new one
os.replace(temp_path, self._stats_file_path)
logger.debug(f"Saved usage statistics to {self._stats_file_path}")
return True
except Exception as e:
logger.error(f"Error saving usage statistics: {e}", exc_info=True)
return False
def register_execution(self, prompt_id):
"""Register a completed execution by prompt_id for later processing"""
if prompt_id:
self.pending_prompt_ids.add(prompt_id)
async def _background_processor(self):
"""Background task to process queued prompt_ids"""
try:
while True:
# Wait a short interval before checking for new prompt_ids
await asyncio.sleep(5) # Check every 5 seconds
# Process any pending prompt_ids
if self.pending_prompt_ids:
async with self._lock:
# Get a copy of the set and clear original
prompt_ids = self.pending_prompt_ids.copy()
self.pending_prompt_ids.clear()
# Process each prompt_id
registry = MetadataRegistry()
for prompt_id in prompt_ids:
try:
metadata = registry.get_metadata(prompt_id)
await self._process_metadata(metadata)
except Exception as e:
logger.error(f"Error processing prompt_id {prompt_id}: {e}")
# Periodically save stats
await self.save_stats()
except asyncio.CancelledError:
# Task was cancelled, clean up
await self.save_stats(force=True)
except Exception as e:
logger.error(f"Error in background processing task: {e}", exc_info=True)
# Restart the task after a delay if it fails
asyncio.create_task(self._restart_background_task())
async def _restart_background_task(self):
"""Restart the background task after a delay"""
await asyncio.sleep(30) # Wait 30 seconds before restarting
self._bg_task = asyncio.create_task(self._background_processor())
async def _process_metadata(self, metadata):
"""Process metadata from an execution"""
if not metadata or not isinstance(metadata, dict):
return
# Increment total executions count
self.stats["total_executions"] += 1
# Get today's date in YYYY-MM-DD format
today = datetime.datetime.now().strftime("%Y-%m-%d")
# Process checkpoints
if MODELS in metadata and isinstance(metadata[MODELS], dict):
await self._process_checkpoints(metadata[MODELS], today)
# Process loras
if LORAS in metadata and isinstance(metadata[LORAS], dict):
await self._process_loras(metadata[LORAS], today)
async def _process_checkpoints(self, models_data, today_date):
"""Process checkpoint models from metadata"""
try:
# Get checkpoint scanner service
checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
if not checkpoint_scanner:
logger.warning("Checkpoint scanner not available for usage tracking")
return
for node_id, model_info in models_data.items():
if not isinstance(model_info, dict):
continue
# Check if this is a checkpoint model
model_type = model_info.get("type")
if model_type == "checkpoint":
model_name = model_info.get("name")
if not model_name:
continue
# Clean up filename (remove extension if present)
model_filename = os.path.splitext(os.path.basename(model_name))[0]
# Get hash for this checkpoint
model_hash = checkpoint_scanner.get_hash_by_filename(model_filename)
if model_hash:
# Update stats for this checkpoint with date tracking
if model_hash not in self.stats["checkpoints"]:
self.stats["checkpoints"][model_hash] = {
"total": 0,
"history": {}
}
# Increment total count
self.stats["checkpoints"][model_hash]["total"] += 1
# Increment today's count
if today_date not in self.stats["checkpoints"][model_hash]["history"]:
self.stats["checkpoints"][model_hash]["history"][today_date] = 0
self.stats["checkpoints"][model_hash]["history"][today_date] += 1
except Exception as e:
logger.error(f"Error processing checkpoint usage: {e}", exc_info=True)
async def _process_loras(self, loras_data, today_date):
"""Process LoRA models from metadata"""
try:
# Get LoRA scanner service
lora_scanner = await ServiceRegistry.get_lora_scanner()
if not lora_scanner:
logger.warning("LoRA scanner not available for usage tracking")
return
for node_id, lora_info in loras_data.items():
if not isinstance(lora_info, dict):
continue
# Get the list of LoRAs from standardized format
lora_list = lora_info.get("lora_list", [])
for lora in lora_list:
if not isinstance(lora, dict):
continue
lora_name = lora.get("name")
if not lora_name:
continue
# Get hash for this LoRA
lora_hash = lora_scanner.get_hash_by_filename(lora_name)
if lora_hash:
# Update stats for this LoRA with date tracking
if lora_hash not in self.stats["loras"]:
self.stats["loras"][lora_hash] = {
"total": 0,
"history": {}
}
# Increment total count
self.stats["loras"][lora_hash]["total"] += 1
# Increment today's count
if today_date not in self.stats["loras"][lora_hash]["history"]:
self.stats["loras"][lora_hash]["history"][today_date] = 0
self.stats["loras"][lora_hash]["history"][today_date] += 1
except Exception as e:
logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
async def get_stats(self):
"""Get current usage statistics"""
return self.stats
async def get_model_usage_count(self, model_type, sha256):
"""Get usage count for a specific model by hash"""
if model_type == "checkpoint":
if sha256 in self.stats["checkpoints"]:
return self.stats["checkpoints"][sha256]["total"]
elif model_type == "lora":
if sha256 in self.stats["loras"]:
return self.stats["loras"][sha256]["total"]
return 0
async def process_execution(self, prompt_id):
"""Process a prompt execution immediately (synchronous approach)"""
if not prompt_id:
return
try:
# Process metadata for this prompt_id
registry = MetadataRegistry()
metadata = registry.get_metadata(prompt_id)
if metadata:
await self._process_metadata(metadata)
# Save stats if needed
await self.save_stats()
except Exception as e:
logger.error(f"Error processing prompt_id {prompt_id}: {e}", exc_info=True)