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"""
Model Management for Neural Reranking.
This module provides sophisticated model management capabilities for neural
reranking including multi-backend support, lazy loading, caching, and
performance optimization for cross-encoder transformer models.
Simplified from reranking/cross_encoder_models.py for integration with
the enhanced neural reranker in the rerankers/ component.
"""
import logging
import time
import os
from typing import Dict, List, Optional, Any, Union
import threading
from dataclasses import dataclass
import numpy as np
logger = logging.getLogger(__name__)
@dataclass
class ModelConfig:
"""Configuration for individual neural reranking models."""
# Model identification
name: str = "cross-encoder/ms-marco-MiniLM-L6-v2"
backend: str = "sentence_transformers" # "sentence_transformers", "tensorflow", "keras", "huggingface_api"
model_type: str = "cross_encoder" # "cross_encoder", "bi_encoder", "ensemble"
# Model parameters
max_length: int = 512
device: str = "auto" # "auto", "cpu", "cuda", "mps"
cache_size: int = 1000
# Performance settings
batch_size: int = 16
optimization_level: str = "balanced" # "speed", "balanced", "quality"
enable_quantization: bool = False
# Model-specific settings
trust_remote_code: bool = False
local_files_only: bool = False
revision: Optional[str] = None
# HuggingFace API settings (for backend="huggingface_api")
api_token: Optional[str] = None
timeout: int = 30
fallback_to_local: bool = True
max_candidates: int = 100
score_threshold: float = 0.0
@dataclass
class ModelInfo:
"""Information about a loaded model."""
name: str
backend: str
device: str
loaded: bool = False
load_time: float = 0.0
inference_count: int = 0
total_inference_time: float = 0.0
last_used: float = 0.0
memory_usage_mb: float = 0.0
error_count: int = 0
class ModelManager:
"""
Manager for individual cross-encoder models.
Handles model loading, caching, and lifecycle management for a single
cross-encoder model with support for multiple backends.
"""
def __init__(self, name: str, config: ModelConfig):
"""
Initialize model manager.
Args:
name: Model identifier
config: Model configuration
"""
self.name = name
self.config = config
self.model = None
self.tokenizer = None
self._lock = threading.Lock()
self.info = ModelInfo(
name=name,
backend=config.backend,
device=config.device
)
logger.info(f"ModelManager created for {name} ({config.backend})")
def load_model(self) -> bool:
"""
Load the model if not already loaded.
Returns:
True if model loaded successfully
"""
with self._lock:
if self.info.loaded:
return True
try:
start_time = time.time()
if self.config.backend == "sentence_transformers":
self._load_sentence_transformer()
elif self.config.backend == "huggingface_api":
self._load_huggingface_api()
else:
raise ValueError(f"Unsupported backend: {self.config.backend}")
load_time = time.time() - start_time
self.info.load_time = load_time
self.info.loaded = True
self.info.last_used = time.time()
logger.info(f"Model {self.name} loaded in {load_time:.2f}s")
return True
except Exception as e:
logger.error(f"Failed to load model {self.name}: {e}")
self.info.error_count += 1
return False
def _resolve_device(self) -> str:
"""
Resolve 'auto' device to appropriate device for current system.
Returns:
Resolved device string ('cpu', 'cuda', 'mps', etc.)
"""
if self.config.device != "auto":
return self.config.device
try:
import torch
# Check for MPS (Metal Performance Shaders) on Apple Silicon
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
return "mps"
# Check for CUDA
if torch.cuda.is_available():
return "cuda"
# Fall back to CPU
return "cpu"
except ImportError:
# If torch is not available, default to CPU
return "cpu"
def _load_sentence_transformer(self):
"""Load model using sentence-transformers."""
try:
from sentence_transformers import CrossEncoder
# Resolve device if set to 'auto'
device = self._resolve_device()
self.model = CrossEncoder(
self.config.name,
max_length=self.config.max_length,
device=device,
trust_remote_code=self.config.trust_remote_code
)
logger.debug(f"Sentence transformer model loaded: {self.config.name} on device: {device}")
except ImportError:
raise ImportError("sentence-transformers library not available")
except Exception as e:
raise RuntimeError(f"Failed to load sentence transformer: {e}")
def _load_huggingface_api(self):
"""Load model using HuggingFace Inference API."""
try:
from huggingface_hub import InferenceClient
# Get API token from config or environment
api_token = (
self.config.api_token or
os.getenv("HF_TOKEN") or
os.getenv("HUGGINGFACE_API_TOKEN") or
os.getenv("HF_API_TOKEN")
)
if not api_token:
raise ValueError("HuggingFace API token required for huggingface_api backend")
# Create inference client
self.model = InferenceClient(token=api_token)
self.api_model_name = self.config.name
logger.debug(f"HuggingFace API client initialized for model: {self.config.name}")
except ImportError:
raise ImportError("huggingface_hub library not available. Install with: pip install huggingface-hub")
except Exception as e:
raise RuntimeError(f"Failed to initialize HuggingFace API client: {e}")
def predict(self, query_doc_pairs: List[List[str]]) -> List[float]:
"""
Generate predictions for query-document pairs.
Args:
query_doc_pairs: List of [query, document] pairs
Returns:
List of relevance scores
"""
if not self.info.loaded and not self.load_model():
raise RuntimeError(f"Model {self.name} not available")
start_time = time.time()
try:
if self.config.backend == "huggingface_api":
scores = self._predict_api(query_doc_pairs)
else:
scores = self._predict_local(query_doc_pairs)
# Update statistics
inference_time = time.time() - start_time
self.info.inference_count += 1
self.info.total_inference_time += inference_time
self.info.last_used = time.time()
return scores
except Exception as e:
self.info.error_count += 1
logger.error(f"Model prediction failed for {self.name}: {e}")
# Try fallback to local if API fails and fallback is enabled
if self.config.backend == "huggingface_api" and self.config.fallback_to_local:
logger.warning(f"API prediction failed, attempting fallback to local model")
try:
return self._fallback_to_local(query_doc_pairs)
except Exception as fallback_error:
logger.error(f"Fallback to local model also failed: {fallback_error}")
raise
def _predict_local(self, query_doc_pairs: List[List[str]]) -> List[float]:
"""
Generate predictions using local model.
Args:
query_doc_pairs: List of [query, document] pairs
Returns:
List of relevance scores
"""
scores = self.model.predict(query_doc_pairs)
# Convert to list if numpy array
if hasattr(scores, 'tolist'):
scores = scores.tolist()
return scores
def _predict_api(self, query_doc_pairs: List[List[str]]) -> List[float]:
"""
Generate predictions using HuggingFace API.
Args:
query_doc_pairs: List of [query, document] pairs
Returns:
List of relevance scores
"""
# Filter by max_candidates if specified
if self.config.max_candidates > 0 and len(query_doc_pairs) > self.config.max_candidates:
query_doc_pairs = query_doc_pairs[:self.config.max_candidates]
logger.debug(f"Filtered to {self.config.max_candidates} candidates for API efficiency")
# Group by query for efficient batch processing
query_groups = {}
for i, (query, document) in enumerate(query_doc_pairs):
if query not in query_groups:
query_groups[query] = []
query_groups[query].append((i, document))
# Process each query group
all_scores = [0.0] * len(query_doc_pairs)
for query, doc_pairs in query_groups.items():
try:
# Prepare documents for this query
documents = []
indices = []
for idx, document in doc_pairs:
# Truncate document if too long
if len(document) > self.config.max_length:
document = document[:self.config.max_length - 50] + "..."
documents.append(document)
indices.append(idx)
# Use HuggingFace API for cross-encoder text ranking
# Format: {"inputs": {"source_sentence": "query", "sentences": ["doc1", "doc2", ...]}}
import requests
api_url = f"https://api-inference.huggingface.co/models/{self.api_model_name}"
headers = {"Authorization": f"Bearer {self.config.api_token}"}
payload = {
"inputs": {
"source_sentence": query,
"sentences": documents
}
}
response = requests.post(api_url, headers=headers, json=payload, timeout=self.config.timeout)
if response.status_code == 200:
result = response.json()
# Extract scores from response
if isinstance(result, list) and len(result) == len(documents):
for i, score in enumerate(result):
if isinstance(score, dict) and 'score' in score:
all_scores[indices[i]] = float(score['score'])
elif isinstance(score, (int, float)):
all_scores[indices[i]] = float(score)
else:
all_scores[indices[i]] = 0.0
else:
logger.warning(f"Unexpected API response format: {result}")
for idx in indices:
all_scores[idx] = 0.0
else:
logger.warning(f"API request failed: {response.status_code} - {response.text}")
for idx in indices:
all_scores[idx] = 0.0
except Exception as e:
logger.warning(f"API prediction failed for query '{query}': {e}")
for idx in indices:
all_scores[idx] = 0.0
# Apply score threshold filtering
if self.config.score_threshold > 0:
all_scores = [max(score, self.config.score_threshold) for score in all_scores]
return all_scores
def _fallback_to_local(self, query_doc_pairs: List[List[str]]) -> List[float]:
"""
Fallback to local model when API fails.
Args:
query_doc_pairs: List of [query, document] pairs
Returns:
List of relevance scores
"""
logger.info("Attempting fallback to local sentence-transformers model")
# Temporarily switch to local backend
original_backend = self.config.backend
self.config.backend = "sentence_transformers"
try:
# Unload API client
self.model = None
self.info.loaded = False
# Load local model
if self.load_model():
scores = self._predict_local(query_doc_pairs)
logger.info("Successfully fell back to local model")
return scores
else:
raise RuntimeError("Failed to load local fallback model")
finally:
# Restore original backend
self.config.backend = original_backend
def unload_model(self):
"""Unload the model to free memory."""
with self._lock:
if self.info.loaded:
self.model = None
self.tokenizer = None
self.info.loaded = False
logger.info(f"Model {self.name} unloaded")
def get_info(self) -> ModelInfo:
"""Get model information."""
return self.info
def get_stats(self) -> Dict[str, Any]:
"""Get model statistics."""
avg_inference_time = 0.0
if self.info.inference_count > 0:
avg_inference_time = self.info.total_inference_time / self.info.inference_count
return {
"name": self.name,
"loaded": self.info.loaded,
"inference_count": self.info.inference_count,
"avg_inference_time_ms": avg_inference_time * 1000,
"total_inference_time": self.info.total_inference_time,
"error_count": self.info.error_count,
"last_used": self.info.last_used
}
class CrossEncoderModels:
"""
Multi-model manager for cross-encoder models.
Manages multiple cross-encoder models with lazy loading, caching,
and automatic model selection based on configuration.
"""
def __init__(self, models_config: Dict[str, ModelConfig]):
"""
Initialize cross-encoder models manager.
Args:
models_config: Dictionary of model configurations
"""
self.models_config = models_config
self.managers: Dict[str, ModelManager] = {}
self.default_model = None
# Create model managers
for name, config in models_config.items():
self.managers[name] = ModelManager(name, config)
# Set default model
if models_config:
self.default_model = list(models_config.keys())[0]
self.stats = {
"total_predictions": 0,
"model_switches": 0,
"cache_hits": 0,
"cache_misses": 0
}
logger.info(f"CrossEncoderModels initialized with {len(models_config)} models")
def predict(
self,
query_doc_pairs: List[List[str]],
model_name: Optional[str] = None
) -> List[float]:
"""
Generate predictions using specified or default model.
Args:
query_doc_pairs: List of [query, document] pairs
model_name: Name of model to use (defaults to default_model)
Returns:
List of relevance scores
"""
if not query_doc_pairs:
return []
# Select model
selected_model = model_name or self.default_model
if selected_model not in self.managers:
logger.warning(f"Model {selected_model} not found, using default")
selected_model = self.default_model
if not selected_model:
raise RuntimeError("No models available")
try:
manager = self.managers[selected_model]
scores = manager.predict(query_doc_pairs)
self.stats["total_predictions"] += 1
return scores
except Exception as e:
logger.error(f"Prediction failed with model {selected_model}: {e}")
# Try fallback to default model if different
if selected_model != self.default_model:
logger.info(f"Trying fallback to default model: {self.default_model}")
return self.predict(query_doc_pairs, self.default_model)
else:
raise
def get_available_models(self) -> List[str]:
"""Get list of available model names."""
return list(self.managers.keys())
def is_model_loaded(self, model_name: str) -> bool:
"""Check if a model is loaded."""
if model_name in self.managers:
return self.managers[model_name].info.loaded
return False
def load_model(self, model_name: str) -> bool:
"""
Load a specific model.
Args:
model_name: Name of model to load
Returns:
True if loaded successfully
"""
if model_name in self.managers:
return self.managers[model_name].load_model()
return False
def unload_model(self, model_name: str):
"""Unload a specific model."""
if model_name in self.managers:
self.managers[model_name].unload_model()
def unload_all_models(self):
"""Unload all models to free memory."""
for manager in self.managers.values():
manager.unload_model()
def get_model_stats(self) -> Dict[str, Dict[str, Any]]:
"""Get statistics for all models."""
return {name: manager.get_stats() for name, manager in self.managers.items()}
def get_stats(self) -> Dict[str, Any]:
"""Get overall statistics."""
stats = self.stats.copy()
stats["models"] = self.get_model_stats()
stats["total_models"] = len(self.managers)
stats["loaded_models"] = sum(1 for m in self.managers.values() if m.info.loaded)
return stats
def reset_stats(self) -> None:
"""Reset statistics."""
self.stats = {
"total_predictions": 0,
"model_switches": 0,
"cache_hits": 0,
"cache_misses": 0
} |