import time from transformers import TFAutoModel, AutoTokenizer import tensorflow as tf import numpy as np import threading from queue import Queue, Empty from typing import Generator, List, Tuple, Dict, Optional, Union, Any import math from dataclasses import dataclass import json from pathlib import Path import datetime import faiss import gc import random from response_quality_checker import ResponseQualityChecker from cross_encoder_reranker import CrossEncoderReranker from conversation_summarizer import DeviceAwareModel, Summarizer from gpu_monitor import GPUMemoryMonitor import absl.logging from logger_config import config_logger from tqdm.auto import tqdm absl.logging.set_verbosity(absl.logging.WARNING) logger = config_logger(__name__) @dataclass class ChatbotConfig: """Configuration for the RetrievalChatbot.""" vocab_size: int = 30526 # DistilBERT vocab size + special tokens max_context_token_limit: int = 512 embedding_dim: int = 512 encoder_units: int = 256 num_attention_heads: int = 8 dropout_rate: float = 0.2 l2_reg_weight: float = 0.001 margin: float = 0.3 learning_rate: float = 0.001 min_text_length: int = 3 max_context_turns: int = 5 warmup_steps: int = 200 pretrained_model: str = 'distilbert-base-uncased' dtype: str = 'float32' freeze_embeddings: bool = False # Additional configurations can be added here def to_dict(self) -> dict: """Convert config to dictionary.""" return {k: str(v) if isinstance(v, Path) else v for k, v in self.__dict__.items()} @classmethod def from_dict(cls, config_dict: dict) -> 'ChatbotConfig': """Create config from dictionary.""" return cls(**{k: v for k, v in config_dict.items() if k in cls.__dataclass_fields__}) class EncoderModel(tf.keras.Model): """Dual encoder model with pretrained embeddings.""" def __init__( self, config: ChatbotConfig, name: str = "encoder", shared_weights: bool = False, **kwargs ): super().__init__(name=name, **kwargs) self.config = config self.shared_weights = shared_weights # Load pretrained model self.pretrained = TFAutoModel.from_pretrained(config.pretrained_model) # Freeze pretrained weights if specified self.pretrained.distilbert.embeddings.trainable = False for i, layer_module in enumerate(self.pretrained.distilbert.transformer.layer): if i < 1: # freeze first layer layer_module.trainable = False else: layer_module.trainable = True # Pooling layer (Global Average Pooling) self.pooler = tf.keras.layers.GlobalAveragePooling1D() # Projection layer self.projection = tf.keras.layers.Dense( config.embedding_dim, activation='tanh', name="projection" ) # Dropout and normalization self.dropout = tf.keras.layers.Dropout(config.dropout_rate) self.normalize = tf.keras.layers.Lambda( lambda x: tf.nn.l2_normalize(x, axis=1) ) def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor: """Forward pass.""" # Get pretrained embeddings pretrained_outputs = self.pretrained(inputs, training=training) x = pretrained_outputs.last_hidden_state # Shape: [batch_size, seq_len, embedding_dim] # Apply pooling, projection, dropout, and normalization x = self.pooler(x) # Shape: [batch_size, 768] x = self.projection(x) # Shape: [batch_size, 512] x = self.dropout(x, training=training) # Apply dropout x = self.normalize(x) # Shape: [batch_size, 512] return x def get_config(self) -> dict: """Return the config of the model.""" config = super().get_config() config.update({ "config": self.config.to_dict(), "shared_weights": self.shared_weights, "name": self.name }) return config class RetrievalChatbot(DeviceAwareModel): """Retrieval-based chatbot using pretrained embeddings and FAISS for similarity search.""" def __init__(self, config: ChatbotConfig, dialogues: List[dict] = [], device: str = None, strategy=None, reranker: Optional[CrossEncoderReranker] = None, summarizer: Optional[Summarizer] = None ): self.config = config self.strategy = strategy self.setup_device(device) if reranker is None: logger.info("Creating default CrossEncoderReranker...") reranker = CrossEncoderReranker(model_name="cross-encoder/ms-marco-MiniLM-L-12-v2") self.reranker = reranker if summarizer is None: logger.info("Creating default Summarizer...") summarizer = Summarizer(device=self.device) self.summarizer = summarizer # # Configure XLA optimization if on GPU/TPU # if self.device in ["GPU", "TPU"]: # tf.config.optimizer.set_jit(True) # logger.info(f"XLA compilation enabled for {self.device}") # # Configure mixed precision for GPU/TPU # if self.device != "CPU": # policy = tf.keras.mixed_precision.Policy('mixed_float16') # tf.keras.mixed_precision.set_global_policy(policy) # logger.info("Mixed precision training enabled (float16)") # Special tokens self.special_tokens = { "user": "", "assistant": "", "context": "", "sep": "" } # Initialize tokenizer and add special tokens self.tokenizer = AutoTokenizer.from_pretrained(config.pretrained_model) self.tokenizer.add_special_tokens( {'additional_special_tokens': list(self.special_tokens.values())} ) self.memory_monitor = GPUMemoryMonitor() self.min_batch_size = 8 self.max_batch_size = 128 self.current_batch_size = 32 # Collect unique responses from dialogues self.response_pool, self.unique_responses = self._collect_responses(dialogues) # Initialize training history self.history = { "train_loss": [], "val_loss": [], "train_metrics": {}, "val_metrics": {} } def build_models(self): """Initialize the shared encoder.""" logger.info("Building encoder model...") tf.keras.backend.clear_session() # Shared encoder for both queries and responses self.encoder = EncoderModel( self.config, name="shared_encoder", ) # Resize token embeddings after adding special tokens new_vocab_size = len(self.tokenizer) self.encoder.pretrained.resize_token_embeddings(new_vocab_size) logger.info(f"Token embeddings resized to: {new_vocab_size}") # Initialize FAISS index (moved here from __init__) self._initialize_faiss() # Compute embeddings after FAISS is initialized and moved self._compute_and_index_embeddings() # Try different ways to get embedding dimension try: # First try: from config embedding_dim = self.encoder.pretrained.config.dim logger.info("Got embedding dim from config") except AttributeError: try: # Second try: from word embeddings embedding_dim = self.encoder.pretrained.distilbert.embeddings.word_embeddings.embedding_dim logger.info("Got embedding dim from word embeddings") except AttributeError: try: # Third try: from embeddings module embedding_dim = self.encoder.pretrained.distilbert.embeddings.embedding_dim logger.info("Got embedding dim from embeddings module") except AttributeError: # Fallback to config value embedding_dim = self.config.embedding_dim logger.info("Using config embedding dim") vocab_size = len(self.tokenizer) logger.info(f"Encoder Embedding Dimension: {embedding_dim}") logger.info(f"Encoder Embedding Vocabulary Size: {vocab_size}") if vocab_size >= embedding_dim: logger.info("Encoder model built and embeddings resized successfully.") else: logger.error("Vocabulary size is less than embedding dimension.") raise ValueError("Vocabulary size is less than embedding dimension.") def _collect_responses(self, dialogues: List[dict]) -> Tuple[List[str], List[str]]: """Collect all unique responses from dialogues.""" logger.info("Collecting responses from dialogues...") responses = [] try: progress_bar = tqdm(dialogues, desc="Collecting assistant responses") except ImportError: progress_bar = dialogues logger.info("Progress bar disabled - continuing without visual progress") for dialogue in progress_bar: turns = dialogue.get('turns', []) for turn in turns: if turn.get('speaker') == 'assistant' and 'text' in turn: responses.append(turn['text'].strip()) # Remove duplicates unique_responses = list(set(responses)) logger.info(f"Found {len(unique_responses)} unique responses.") return responses, unique_responses def _adjust_batch_size(self) -> None: """Dynamically adjust batch size based on GPU memory usage.""" if self.memory_monitor.should_reduce_batch_size(): new_size = max(self.min_batch_size, self.current_batch_size // 2) if new_size != self.current_batch_size: logger.info(f"Reducing batch size to {new_size} due to high memory usage") self.current_batch_size = new_size gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() elif self.memory_monitor.can_increase_batch_size(): new_size = min(self.max_batch_size, self.current_batch_size * 2) if new_size != self.current_batch_size: logger.info(f"Increasing batch size to {new_size}") self.current_batch_size = new_size def _initialize_faiss(self): """Initialize FAISS with safer GPU handling and memory monitoring.""" logger.info("Initializing FAISS index...") # First, detect if we have GPU-enabled FAISS self.faiss_gpu = False self.gpu_resources = [] try: if hasattr(faiss, 'get_num_gpus'): ngpus = faiss.get_num_gpus() if ngpus > 0: # Configure GPU resources with memory limit for i in range(ngpus): res = faiss.StandardGpuResources() # Set temp memory to 1/4 of total memory to avoid OOM if self.memory_monitor.has_gpu: stats = self.memory_monitor.get_memory_stats() if stats: temp_memory = int(stats.total * 0.25) # 25% of total memory res.setTempMemory(temp_memory) self.gpu_resources.append(res) self.faiss_gpu = True logger.info(f"FAISS GPU resources initialized on {ngpus} GPUs") else: logger.info("Using CPU-only FAISS build") except Exception as e: logger.warning(f"Using CPU due to GPU initialization error: {e}") # TODO: figure out buf with faiss-gpu try: # Create appropriate index based on dataset size if len(self.unique_responses) < 1000: logger.info("Small dataset detected, using simple FlatIP index") self.index = faiss.IndexFlatIP(self.config.embedding_dim) else: # Use IVF index with dynamic number of clusters # nlist = min( # 25, # max clusters # max(int(math.sqrt(len(self.unique_responses))), 1) # min 1 cluster # ) # logger.info(f"Using IVF index with {nlist} clusters") # quantizer = faiss.IndexFlatIP(self.config.embedding_dim) # self.index = faiss.IndexIVFFlat( # quantizer, # self.config.embedding_dim, # nlist, # faiss.METRIC_INNER_PRODUCT # ) self.index = faiss.IndexFlatIP(self.config.embedding_dim) # # Move to GPU(s) if available # if self.faiss_gpu and self.gpu_resources: # try: # if len(self.gpu_resources) > 1: # self.index = faiss.index_cpu_to_gpus_list(self.index, self.gpu_resources) # logger.info("FAISS index distributed across multiple GPUs") # else: # self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, self.index) # logger.info("FAISS index moved to single GPU") # except Exception as e: # logger.warning(f"Failed to move index to GPU: {e}. Falling back to CPU") # self.faiss_gpu = False # # Set search parameters for IVF index # if isinstance(self.index, faiss.IndexIVFFlat): # self.index.nprobe = min(10, nlist) except Exception as e: logger.error(f"Error initializing FAISS: {e}") raise def encode_responses( self, responses: List[str], batch_size: int = 64 ) -> tf.Tensor: """ Encodes responses with more conservative memory management. """ all_embeddings = [] self.current_batch_size = batch_size # Memory stats # if self.memory_monitor.has_gpu: # initial_stats = self.memory_monitor.get_memory_stats() # if initial_stats: # logger.info("Initial GPU memory state:") # logger.info(f"Total: {initial_stats.total / 1e9:.2f}GB") # logger.info(f"Used: {initial_stats.used / 1e9:.2f}GB") # logger.info(f"Free: {initial_stats.free / 1e9:.2f}GB") total_processed = 0 with tqdm(total=len(responses), desc="Encoding responses") as pbar: while total_processed < len(responses): # Monitor memory and adjust batch size if self.memory_monitor.has_gpu: gpu_usage = self.memory_monitor.get_memory_usage() if gpu_usage > 0.8: # Over 80% usage self.current_batch_size = max(128, self.current_batch_size // 2) logger.info(f"High GPU memory usage ({gpu_usage:.1%}), reducing batch size to {self.current_batch_size}") gc.collect() tf.keras.backend.clear_session() # Get batch end_idx = min(total_processed + self.current_batch_size, len(responses)) batch_texts = responses[total_processed:end_idx] try: # Tokenize encodings = self.tokenizer( batch_texts, padding='max_length', truncation=True, max_length=self.config.max_context_token_limit, return_tensors='tf' ) # Encode embeddings_batch = self.encoder(encodings['input_ids'], training=False) # Cast to float32 if embeddings_batch.dtype != tf.float32: embeddings_batch = tf.cast(embeddings_batch, tf.float32) # Store all_embeddings.append(embeddings_batch) # Update progress batch_processed = len(batch_texts) total_processed += batch_processed # Update progress bar if self.memory_monitor.has_gpu: gpu_usage = self.memory_monitor.get_memory_usage() pbar.set_postfix({ 'GPU mem': f'{gpu_usage:.1%}', 'batch_size': self.current_batch_size }) pbar.update(batch_processed) # Memory cleanup every 1000 samples if total_processed % 1000 == 0: gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() except tf.errors.ResourceExhaustedError: logger.warning("GPU memory exhausted during encoding, reducing batch size") self.current_batch_size = max(8, self.current_batch_size // 2) continue except Exception as e: logger.error(f"Error during encoding: {str(e)}") raise # Concatenate results #logger.info("Concatenating embeddings...") if len(all_embeddings) == 1: final_embeddings = all_embeddings[0] else: final_embeddings = tf.concat(all_embeddings, axis=0) return final_embeddings def _train_faiss_index(self, response_embeddings: np.ndarray) -> None: """Train FAISS index with better memory management and robust fallback mechanisms.""" if self.index.is_trained: logger.info("Index already trained, skipping training phase") return logger.info("Starting FAISS index training...") try: # First attempt: Try training with smaller subset subset_size = min(5000, len(response_embeddings)) # Reduced from 10000 logger.info(f"Using {subset_size} samples for initial training attempt") subset_idx = np.random.choice(len(response_embeddings), subset_size, replace=False) training_embeddings = response_embeddings[subset_idx].copy() # Make a copy # Ensure contiguous memory layout training_embeddings = np.ascontiguousarray(training_embeddings) # Force cleanup before training gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() # Verify data properties logger.info(f"FAISS training data shape: {training_embeddings.shape}") logger.info(f"FAISS training data dtype: {training_embeddings.dtype}") logger.info("Starting initial training attempt...") self.index.train(training_embeddings) logger.info("Training completed successfully") except (RuntimeError, Exception) as e: logger.warning(f"Initial training attempt failed: {str(e)}") logger.info("Attempting fallback strategy...") try: # Move to CPU for more stable training if self.faiss_gpu: logger.info("Moving index to CPU for fallback training") cpu_index = faiss.index_gpu_to_cpu(self.index) else: cpu_index = self.index # Create simpler index type if needed if isinstance(cpu_index, faiss.IndexIVFFlat): logger.info("Creating simpler FlatL2 index for fallback") cpu_index = faiss.IndexFlatL2(self.config.embedding_dim) # Use even smaller subset for CPU training subset_size = min(2000, len(response_embeddings)) subset_idx = np.random.choice(len(response_embeddings), subset_size, replace=False) fallback_embeddings = response_embeddings[subset_idx].copy() # Ensure data is properly formatted if not fallback_embeddings.flags['C_CONTIGUOUS']: fallback_embeddings = np.ascontiguousarray(fallback_embeddings) if fallback_embeddings.dtype != np.float32: fallback_embeddings = fallback_embeddings.astype(np.float32) # Train on CPU logger.info("Training fallback index on CPU...") cpu_index.train(fallback_embeddings) # Move back to GPU if needed if self.faiss_gpu: logger.info("Moving trained index back to GPU...") if len(self.gpu_resources) > 1: self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources) else: self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index) else: self.index = cpu_index logger.info("Fallback training completed successfully") except Exception as e2: logger.error(f"Fallback training also failed: {str(e2)}") logger.warning("Creating basic brute-force index as last resort") try: # Create basic brute-force index as last resort dim = response_embeddings.shape[1] basic_index = faiss.IndexFlatL2(dim) if self.faiss_gpu: if len(self.gpu_resources) > 1: self.index = faiss.index_cpu_to_gpus_list(basic_index, self.gpu_resources) else: self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, basic_index) else: self.index = basic_index logger.info("Basic index created as fallback") except Exception as e3: logger.error(f"All training attempts failed: {str(e3)}") raise RuntimeError("Unable to create working FAISS index") def _add_vectors_to_index(self, response_embeddings: np.ndarray) -> None: """Add vectors to FAISS index with enhanced memory management.""" logger.info("Starting vector addition process...") # Even smaller batches initial_batch_size = 50 # Start smaller min_batch_size = 10 max_batch_size = 500 # Lower maximum total_added = 0 retry_count = 0 max_retries = 5 while total_added < len(response_embeddings): try: # Monitor memory if self.memory_monitor.has_gpu: gpu_usage = self.memory_monitor.get_memory_usage() #logger.info(f"GPU memory usage before batch: {gpu_usage:.1%}") # Force cleanup if memory usage is high if gpu_usage > 0.7: # Lower threshold to 70% logger.info("High memory usage detected, forcing cleanup") gc.collect() tf.keras.backend.clear_session() # Get batch end_idx = min(total_added + initial_batch_size, len(response_embeddings)) batch = response_embeddings[total_added:end_idx] # Add batch self.index.add(batch) # Update progress batch_size = len(batch) total_added += batch_size #logger.info(f"Added batch of {batch_size} vectors ({total_added}/{len(response_embeddings)} total)") # Memory cleanup every few batches if total_added % (initial_batch_size * 5) == 0: gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() # Gradually increase batch size if initial_batch_size < max_batch_size: initial_batch_size = min(initial_batch_size + 25, max_batch_size) except Exception as e: logger.warning(f"Error adding batch: {str(e)}") retry_count += 1 if retry_count > max_retries: logger.error("Max retries exceeded.") raise # Reduce batch size initial_batch_size = max(min_batch_size, initial_batch_size // 2) logger.info(f"Reducing batch size to {initial_batch_size} and retrying...") # Cleanup gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() time.sleep(1) # Brief pause before retry logger.info(f"Successfully added all {total_added} vectors to index") def _add_vectors_cpu_fallback(self, remaining_embeddings: np.ndarray, already_added: int = 0) -> None: """CPU fallback with extra safeguards and progress tracking.""" logger.info(f"CPU Fallback: Adding {len(remaining_embeddings)} remaining vectors...") try: # Move index to CPU if self.faiss_gpu: logger.info("Moving index to CPU...") cpu_index = faiss.index_gpu_to_cpu(self.index) else: cpu_index = self.index # Add remaining vectors on CPU with very small batches batch_size = 50 # Extremely conservative batch size for CPU total_added = already_added for i in range(0, len(remaining_embeddings), batch_size): end_idx = min(i + batch_size, len(remaining_embeddings)) batch = remaining_embeddings[i:end_idx] # Add batch cpu_index.add(batch) # Update progress total_added += len(batch) if i % (batch_size * 10) == 0: logger.info(f"Added {total_added} vectors total " f"({i}/{len(remaining_embeddings)} in current phase)") # Periodic cleanup if i % (batch_size * 20) == 0: gc.collect() # Move back to GPU if needed if self.faiss_gpu: logger.info("Moving index back to GPU...") if len(self.gpu_resources) > 1: self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources) else: self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index) else: self.index = cpu_index logger.info("CPU fallback completed successfully") except Exception as e: logger.error(f"Error during CPU fallback: {str(e)}") raise def _compute_and_index_embeddings(self): """Compute embeddings and build FAISS index with simpler handling.""" logger.info("Computing embeddings and indexing with FAISS...") try: # Encode responses with memory monitoring logger.info("Encoding unique responses") response_embeddings = self.encode_responses(self.unique_responses) response_embeddings = response_embeddings.numpy() # Memory cleanup after encoding gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() # Ensure float32 and memory contiguous response_embeddings = response_embeddings.astype('float32') response_embeddings = np.ascontiguousarray(response_embeddings) # Log memory state before normalization if self.memory_monitor.has_gpu: stats = self.memory_monitor.get_memory_stats() if stats: logger.info(f"GPU memory before normalization: {stats.used/1e9:.2f}GB used") # Normalize embeddings logger.info("Normalizing embeddings with FAISS") faiss.normalize_L2(response_embeddings) # Create and initialize simple FlatIP index dim = response_embeddings.shape[1] if self.faiss_gpu: cpu_index = faiss.IndexFlatIP(dim) if len(self.gpu_resources) > 1: self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources) else: self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index) else: self.index = faiss.IndexFlatIP(dim) # Add vectors to index self._add_vectors_to_index(response_embeddings) # Store responses and embeddings self.response_pool = self.unique_responses self.response_embeddings = response_embeddings # Final memory cleanup gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() # Log final state logger.info(f"Successfully indexed {self.index.ntotal} responses") if self.memory_monitor.has_gpu: stats = self.memory_monitor.get_memory_stats() if stats: logger.info(f"Final GPU memory usage: {stats.used/1e9:.2f}GB used") logger.info("Indexing completed successfully") except Exception as e: logger.error(f"Error during indexing: {e}") # Ensure cleanup even on error gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() raise def verify_faiss_index(self): """Verify that FAISS index matches the response pool.""" indexed_size = self.index.ntotal pool_size = len(self.response_pool) logger.info(f"FAISS index size: {indexed_size}") logger.info(f"Response pool size: {pool_size}") if indexed_size != pool_size: logger.warning("Mismatch between FAISS index size and response pool size.") else: logger.info("FAISS index correctly matches the response pool.") def encode_query(self, query: str, context: Optional[List[Tuple[str, str]]] = None) -> tf.Tensor: """Encode a query with optional conversation context.""" # Prepare query with context if context: context_str = ' '.join([ f"{self.special_tokens['user']} {q} " f"{self.special_tokens['assistant']} {r}" for q, r in context[-self.config.max_context_turns:] ]) query = f"{context_str} {self.special_tokens['user']} {query}" else: query = f"{self.special_tokens['user']} {query}" # Tokenize and encode encodings = self.tokenizer( [query], padding='max_length', truncation=True, max_length=self.config.max_context_token_limit, return_tensors='tf' ) input_ids = encodings['input_ids'] # Verify token IDs max_id = tf.reduce_max(input_ids).numpy() new_vocab_size = len(self.tokenizer) if max_id >= new_vocab_size: logger.error(f"Token ID {max_id} exceeds the vocabulary size {new_vocab_size}.") raise ValueError("Token ID exceeds vocabulary size.") # Get embeddings from the shared encoder return self.encoder(input_ids, training=False) def retrieve_responses_cross_encoder( self, query: str, top_k: int, reranker: Optional[CrossEncoderReranker] = None, summarizer: Optional[Summarizer] = None, summarize_threshold: int = 512 # Summarize over 512 tokens ) -> List[Tuple[str, float]]: """ Retrieve top-k from FAISS, then re-rank them with a cross-encoder. Optionally summarize the user query if it's too long. """ if reranker is None: reranker = self.reranker if summarizer is None: summarizer = self.summarizer # Optional summarization if summarizer and len(query.split()) > summarize_threshold: logger.info(f"Query is long. Summarizing before cross-encoder. Original length: {len(query.split())}") query = summarizer.summarize_text(query) logger.info(f"Summarized query: {query}") # 2) Dense retrieval dense_topk = self.retrieve_responses_faiss(query, top_k=top_k) # [(resp, dense_score), ...] if not dense_topk: return [] # 3) Cross-encoder rerank candidate_texts = [pair[0] for pair in dense_topk] cross_scores = reranker.rerank(query, candidate_texts, max_length=256) # Combine combined = [(text, score) for (text, _), score in zip(dense_topk, cross_scores)] # Sort descending by cross-encoder score combined.sort(key=lambda x: x[1], reverse=True) return combined def retrieve_responses_faiss(self, query: str, top_k: int = 5) -> List[Tuple[str, float]]: """Retrieve top-k responses using FAISS.""" # Encode the query q_emb = self.encode_query(query) # Shape: [1, embedding_dim] q_emb_np = q_emb.numpy().astype('float32') # Ensure type match # Normalize the query embedding for cosine similarity faiss.normalize_L2(q_emb_np) # Search the FAISS index distances, indices = self.index.search(q_emb_np, top_k) # Map indices to responses and distances to similarities top_responses = [] for i, idx in enumerate(indices[0]): if idx < len(self.response_pool): top_responses.append((self.response_pool[idx], float(distances[0][i]))) else: logger.warning(f"FAISS returned invalid index {idx}. Skipping.") return top_responses def save_models(self, save_dir: Union[str, Path]): """Save models and configuration.""" save_dir = Path(save_dir) save_dir.mkdir(parents=True, exist_ok=True) # Save config with open(save_dir / "config.json", "w") as f: json.dump(self.config.to_dict(), f, indent=2) # Save models self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder") # Save tokenizer self.tokenizer.save_pretrained(save_dir / "tokenizer") logger.info(f"Models and tokenizer saved to {save_dir}.") @classmethod def load_models(cls, load_dir: Union[str, Path]) -> 'RetrievalChatbot': """Load saved models and configuration.""" load_dir = Path(load_dir) # Load config with open(load_dir / "config.json", "r") as f: config = ChatbotConfig.from_dict(json.load(f)) # Initialize chatbot chatbot = cls(config) # Load models chatbot.encoder.pretrained = TFAutoModel.from_pretrained( load_dir / "shared_encoder", config=config ) # Load tokenizer chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer") logger.info(f"Models and tokenizer loaded from {load_dir}.") return chatbot @staticmethod def load_training_data(data_path: Union[str, Path], debug_samples: Optional[int] = None) -> List[dict]: """ Load training data from a JSON file. Args: data_path (Union[str, Path]): Path to the JSON file containing dialogues. debug_samples (Optional[int]): Number of samples to load for debugging. Returns: List[dict]: List of dialogue dictionaries. """ logger.info(f"Loading training data from {data_path}...") data_path = Path(data_path) if not data_path.exists(): logger.error(f"Data file {data_path} does not exist.") return [] with open(data_path, 'r', encoding='utf-8') as f: dialogues = json.load(f) if debug_samples is not None: dialogues = dialogues[:debug_samples] logger.info(f"Debug mode: Limited to {debug_samples} dialogues") logger.info(f"Loaded {len(dialogues)} dialogues.") return dialogues def train_streaming( self, dialogues: List[dict], epochs: int = 20, batch_size: int = 16, validation_split: float = 0.2, checkpoint_dir: str = "checkpoints/", use_lr_schedule: bool = True, peak_lr: float = 2e-5, warmup_steps_ratio: float = 0.1, early_stopping_patience: int = 3, min_delta: float = 1e-4, buffer_size: int = 10, neg_samples: int = 1 ) -> None: """ Streaming version of training that interleaves training/val batches by giving priority to training until we meet `steps_per_epoch`, then sending leftover batches to validation. """ logger.info("Starting streaming training pipeline...") # Initialize dataset preparer dataset_preparer = StreamingDataPipeline( tokenizer=self.tokenizer, encoder=self.encoder, index=self.index, response_pool=self.response_pool, max_length=self.config.max_context_token_limit, batch_size=batch_size, neg_samples=neg_samples ) # Calculate total steps for learning rate schedule total_pairs = dataset_preparer.estimate_total_pairs(dialogues) train_size = total_pairs * (1 - validation_split) steps_per_epoch = int(math.ceil(train_size / batch_size)) val_steps = int(math.ceil((total_pairs * validation_split) / batch_size)) total_steps = steps_per_epoch * epochs logger.info(f"Total pairs: {total_pairs}") logger.info(f"Training pairs: {train_size}") logger.info(f"Steps per epoch: {steps_per_epoch}") logger.info(f"Validation steps: {val_steps}") logger.info(f"Total steps: {total_steps}") # Set up optimizer with learning rate schedule if use_lr_schedule: warmup_steps = int(total_steps * warmup_steps_ratio) lr_schedule = self._get_lr_schedule( total_steps=total_steps, peak_lr=peak_lr, warmup_steps=warmup_steps ) self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule) logger.info("Using custom learning rate schedule.") else: self.optimizer = tf.keras.optimizers.Adam(learning_rate=peak_lr) logger.info("Using fixed learning rate.") # Initialize checkpoint manager checkpoint = tf.train.Checkpoint(optimizer=self.optimizer, model=self.encoder) manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=3) # Setup TensorBoard log_dir = Path(checkpoint_dir) / "tensorboard_logs" log_dir.mkdir(parents=True, exist_ok=True) current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") train_log_dir = str(log_dir / f"train_{current_time}") val_log_dir = str(log_dir / f"val_{current_time}") train_summary_writer = tf.summary.create_file_writer(train_log_dir) val_summary_writer = tf.summary.create_file_writer(val_log_dir) logger.info(f"TensorBoard logs will be saved in {log_dir}") # Training loop best_val_loss = float("inf") epochs_no_improve = 0 try: epoch_pbar = tqdm(range(1, epochs + 1), desc="Training", unit="epoch") is_tqdm_epoch = True except ImportError: epoch_pbar = range(1, epochs + 1) is_tqdm_epoch = False logger.info("Epoch progress bar disabled - continuing without visual progress") for epoch in epoch_pbar: # Shared queues for streaming pipeline train_queue = Queue(maxsize=buffer_size) val_queue = Queue(maxsize=buffer_size) stop_flag = threading.Event() def data_pipeline_worker(): """Thread function that processes dialogues and sends batches to train or val.""" try: train_batches_needed = steps_per_epoch # 9 in your logs val_batches_needed = val_steps # 3 in your logs train_batches_sent = 0 val_batches_sent = 0 logger.info(f"Pipeline starting: need {train_batches_needed} train batches, {val_batches_needed} val batches") # Possibly shuffle your processed pairs to avoid repeating them in the same order # (If you haven't already done so in the pipeline) random.shuffle(dataset_preparer.processed_pairs) while (train_batches_sent < train_batches_needed or val_batches_sent < val_batches_needed): # We loop over the generator for batch in dataset_preparer.process_dialogues(dialogues): if stop_flag.is_set(): logger.warning("Pipeline stopped early") break if train_batches_sent < train_batches_needed: train_queue.put(batch) train_batches_sent += 1 elif val_batches_sent < val_batches_needed: val_queue.put(batch) val_batches_sent += 1 else: # We have enough batches for both train & val break # If we still haven't met our target steps, REPEAT the data if train_batches_sent < train_batches_needed or val_batches_sent < val_batches_needed: logger.info("Data exhausted, repeating since we still need more batches...") # Optionally shuffle again random.shuffle(dataset_preparer.processed_pairs) else: # We have enough break logger.info( f"Pipeline complete: sent {train_batches_sent}/{train_batches_needed} train batches, " f"{val_batches_sent}/{val_batches_needed} val batches" ) except Exception as e: logger.error(f"Error in pipeline worker: {str(e)}") raise e finally: train_queue.put(None) val_queue.put(None) # Start data preparation pipeline in background thread pipeline_thread = threading.Thread(target=data_pipeline_worker) pipeline_thread.start() try: # --- Training Phase --- epoch_loss_avg = tf.keras.metrics.Mean() batches_processed = 0 try: train_pbar = tqdm(total=steps_per_epoch, desc=f"Training Epoch {epoch}") is_tqdm_train = True except ImportError: train_pbar = None is_tqdm_train = False logger.info("Training progress bar disabled") while batches_processed < steps_per_epoch: try: batch = train_queue.get(timeout=1200) # 20 minutes timeout if batch is None: logger.warning(f"Received end signal after only {batches_processed}/{steps_per_epoch} batches") break q_batch, p_batch = batch[0], batch[1] attention_mask = batch[2] if len(batch) > 2 else None loss = self.train_step(q_batch, p_batch, attention_mask) epoch_loss_avg(loss) batches_processed += 1 # Log to TensorBoard with train_summary_writer.as_default(): tf.summary.scalar("loss", loss, step=epoch) # Update progress bar if use_lr_schedule: current_lr = float(lr_schedule(self.optimizer.iterations)) else: current_lr = float(self.optimizer.learning_rate.numpy()) if is_tqdm_train: train_pbar.update(1) train_pbar.set_postfix({ "loss": f"{loss.numpy():.4f}", "lr": f"{current_lr:.2e}", "batches": f"{batches_processed}/{steps_per_epoch}" }) except Empty: logger.warning(f"Queue timeout after {batches_processed}/{steps_per_epoch} batches") break if is_tqdm_train and train_pbar: train_pbar.close() # --- Validation Phase --- val_loss_avg = tf.keras.metrics.Mean() val_batches_processed = 0 try: val_pbar = tqdm(total=val_steps, desc="Validation") is_tqdm_val = True except ImportError: val_pbar = None is_tqdm_val = False logger.info("Validation progress bar disabled") while val_batches_processed < val_steps: try: batch = val_queue.get(timeout=30) if batch is None: logger.warning( f"Received end signal after {val_batches_processed}/{val_steps} validation batches" ) break q_batch, p_batch = batch[0], batch[1] attention_mask = batch[2] if len(batch) > 2 else None val_loss = self.validation_step(q_batch, p_batch, attention_mask) val_loss_avg(val_loss) val_batches_processed += 1 if is_tqdm_val: val_pbar.update(1) val_pbar.set_postfix({ "val_loss": f"{val_loss.numpy():.4f}", "batches": f"{val_batches_processed}/{val_steps}" }) except Empty: logger.warning( f"Validation queue timeout after {val_batches_processed}/{val_steps} batches" ) break if is_tqdm_val and val_pbar: val_pbar.close() # End of epoch: compute final epoch stats train_loss = epoch_loss_avg.result().numpy() val_loss = val_loss_avg.result().numpy() logger.info(f"Epoch {epoch} Complete: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}") # Log epoch metrics with val_summary_writer.as_default(): tf.summary.scalar("val_loss", val_loss, step=epoch) # Save checkpoint manager.save() # Store metrics in history self.history['train_loss'].append(train_loss) self.history['val_loss'].append(val_loss) if use_lr_schedule: current_lr = float(lr_schedule(self.optimizer.iterations)) else: current_lr = float(self.optimizer.learning_rate.numpy()) self.history.setdefault('learning_rate', []).append(current_lr) # Early stopping logic if val_loss < best_val_loss - min_delta: best_val_loss = val_loss epochs_no_improve = 0 logger.info(f"Validation loss improved to {val_loss:.4f}. Reset patience.") else: epochs_no_improve += 1 logger.info(f"No improvement this epoch. Patience: {epochs_no_improve}/{early_stopping_patience}") if epochs_no_improve >= early_stopping_patience: logger.info("Early stopping triggered.") break except Exception as e: logger.error(f"Error during training: {str(e)}") stop_flag.set() raise e finally: # Clean up epoch resources stop_flag.set() pipeline_thread.join() logger.info("Streaming training completed!") @tf.function def train_step(self, q_batch: tf.Tensor, p_batch: tf.Tensor, attention_mask: Optional[tf.Tensor] = None) -> tf.Tensor: """Single training step with tf.function optimization and partial batch handling.""" with tf.GradientTape() as tape: q_enc = self.encoder(q_batch, training=True) p_enc = self.encoder(p_batch, training=True) sim_matrix = tf.matmul(q_enc, p_enc, transpose_b=True) # Handle partial batches batch_size = tf.shape(q_enc)[0] labels = tf.range(batch_size, dtype=tf.int32) loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=sim_matrix ) # If there's an attention mask, apply it if attention_mask is not None: loss = loss * attention_mask # normalize by the sum of attention_mask loss = tf.reduce_sum(loss) / tf.reduce_sum(attention_mask) else: loss = tf.reduce_mean(loss) gradients = tape.gradient(loss, self.encoder.trainable_variables) self.optimizer.apply_gradients(zip(gradients, self.encoder.trainable_variables)) return loss @tf.function def validation_step(self, q_batch: tf.Tensor, p_batch: tf.Tensor, attention_mask: Optional[tf.Tensor] = None) -> tf.Tensor: """Single validation step with partial batch handling.""" q_enc = self.encoder(q_batch, training=False) p_enc = self.encoder(p_batch, training=False) sim_matrix = tf.matmul(q_enc, p_enc, transpose_b=True) batch_size = tf.shape(q_enc)[0] labels = tf.range(batch_size, dtype=tf.int32) loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=sim_matrix ) if attention_mask is not None: loss = loss * attention_mask loss = tf.reduce_sum(loss) / tf.reduce_sum(attention_mask) else: loss = tf.reduce_mean(loss) return loss def _get_lr_schedule( self, total_steps: int, peak_lr: float, warmup_steps: int ) -> tf.keras.optimizers.schedules.LearningRateSchedule: """Create a custom learning rate schedule with warmup and cosine decay.""" class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule): def __init__( self, total_steps: int, peak_lr: float, warmup_steps: int ): super().__init__() self.total_steps = tf.cast(total_steps, tf.float32) self.peak_lr = tf.cast(peak_lr, tf.float32) # Adjust warmup_steps to not exceed half of total_steps adjusted_warmup_steps = min(warmup_steps, max(1, total_steps // 10)) self.warmup_steps = tf.cast(adjusted_warmup_steps, tf.float32) # Calculate and store constants self.initial_lr = self.peak_lr * 0.1 # Start at 10% of peak self.min_lr = self.peak_lr * 0.01 # Minimum 1% of peak logger.info(f"Learning rate schedule initialized:") logger.info(f" Initial LR: {float(self.initial_lr):.6f}") logger.info(f" Peak LR: {float(self.peak_lr):.6f}") logger.info(f" Min LR: {float(self.min_lr):.6f}") logger.info(f" Warmup steps: {int(self.warmup_steps)}") logger.info(f" Total steps: {int(self.total_steps)}") def __call__(self, step): step = tf.cast(step, tf.float32) # Warmup phase warmup_factor = tf.minimum(1.0, step / self.warmup_steps) warmup_lr = self.initial_lr + (self.peak_lr - self.initial_lr) * warmup_factor # Decay phase decay_steps = tf.maximum(1.0, self.total_steps - self.warmup_steps) decay_factor = (step - self.warmup_steps) / decay_steps decay_factor = tf.minimum(tf.maximum(0.0, decay_factor), 1.0) # Clip to [0,1] cosine_decay = 0.5 * (1.0 + tf.cos(tf.constant(math.pi) * decay_factor)) decay_lr = self.min_lr + (self.peak_lr - self.min_lr) * cosine_decay # Choose between warmup and decay final_lr = tf.where(step < self.warmup_steps, warmup_lr, decay_lr) # Ensure learning rate is valid final_lr = tf.maximum(self.min_lr, final_lr) final_lr = tf.where(tf.math.is_finite(final_lr), final_lr, self.min_lr) return final_lr def get_config(self): return { "total_steps": self.total_steps, "peak_lr": self.peak_lr, "warmup_steps": self.warmup_steps, } return CustomSchedule(total_steps, peak_lr, warmup_steps) def _cosine_similarity(self, emb1: np.ndarray, emb2: np.ndarray) -> np.ndarray: """Compute cosine similarity between two numpy arrays.""" normalized_emb1 = emb1 / np.linalg.norm(emb1, axis=1, keepdims=True) normalized_emb2 = emb2 / np.linalg.norm(emb2, axis=1, keepdims=True) return np.dot(normalized_emb1, normalized_emb2.T) def chat( self, query: str, conversation_history: Optional[List[Tuple[str, str]]] = None, quality_checker: Optional['ResponseQualityChecker'] = None, top_k: int = 5, ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]: """ Example chat method that always uses cross-encoder re-ranking if self.reranker is available. """ @self.run_on_device def get_response(self_arg, query_arg): # Add parameters that match decorator's expectations # 1) Build conversation context string conversation_str = self_arg._build_conversation_context(query_arg, conversation_history) # 2) Retrieve + cross-encoder re-rank results = self_arg.retrieve_responses_cross_encoder( query=conversation_str, top_k=top_k, reranker=self_arg.reranker, summarizer=self_arg.summarizer, summarize_threshold=512 ) # 3) Handle empty or confidence if not results: return ( "I'm sorry, but I couldn't find a relevant response.", [], {} ) if quality_checker: metrics = quality_checker.check_response_quality(query_arg, results) if not metrics.get('is_confident', False): return ( "I need more information to provide a good answer. Could you please clarify?", results, metrics ) return results[0][0], results, metrics return results[0][0], results, {} return get_response(self, query) def _build_conversation_context( self, query: str, conversation_history: Optional[List[Tuple[str, str]]] ) -> str: """Build conversation context with better memory management.""" if not conversation_history: return f"{self.special_tokens['user']} {query}" conversation_parts = [] for user_txt, assistant_txt in conversation_history: conversation_parts.extend([ f"{self.special_tokens['user']} {user_txt}", f"{self.special_tokens['assistant']} {assistant_txt}" ]) conversation_parts.append(f"{self.special_tokens['user']} {query}") return "\n".join(conversation_parts) class StreamingDataPipeline: """Helper class to manage the streaming data preparation pipeline with optimized caching and GPU usage.""" def __init__( self, tokenizer, encoder, index, response_pool, max_length: int, batch_size: int, neg_samples: int ): self.tokenizer = tokenizer self.encoder = encoder self.index = index self.response_pool = response_pool self.max_length = max_length self.base_batch_size = batch_size self.neg_samples = neg_samples self.memory_monitor = GPUMemoryMonitor() # Caching structures self.hard_negatives_cache = {} self.processed_pairs = [] self.query_embeddings_cache = {} # Error tracking self.error_count = 0 self.max_retries = 3 # Batch processing settings self.current_batch_size = batch_size self.batch_increase_factor = 1.25 # TODO: use GPU/strategy if len(response_pool) < 100: self.embedding_batch_size = 16 self.search_batch_size = 8 self.max_batch_size = 32 self.min_batch_size = 4 else: self.embedding_batch_size = 64 self.search_batch_size = 32 self.min_batch_size = max(8, batch_size // 4) self.max_batch_size = 64 def save_cache(self, cache_dir: Path) -> None: """Save all cached data for future runs.""" cache_dir = Path(cache_dir) cache_dir.mkdir(parents=True, exist_ok=True) logger.info(f"Saving cache to {cache_dir}") # Save embeddings cache embeddings_path = cache_dir / "query_embeddings.npy" np.save( embeddings_path, {k: v.numpy() if hasattr(v, 'numpy') else v for k, v in self.query_embeddings_cache.items()} ) # Save hard negatives and processed pairs with open(cache_dir / "hard_negatives.json", 'w') as f: json.dump(self.hard_negatives_cache, f) with open(cache_dir / "processed_pairs.json", 'w') as f: json.dump(self.processed_pairs, f) logger.info("Cache saved successfully") def load_cache(self, cache_dir: Path) -> bool: """Load cached data if available.""" cache_dir = Path(cache_dir) required_files = [ "query_embeddings.npy", "hard_negatives.json", "processed_pairs.json" ] if not all((cache_dir / f).exists() for f in required_files): logger.info("Cache files not found") return False try: logger.info("Loading cache...") # Load embeddings self.query_embeddings_cache = np.load( cache_dir / "query_embeddings.npy", allow_pickle=True ).item() # Load other caches with open(cache_dir / "hard_negatives.json", 'r') as f: self.hard_negatives_cache = json.load(f) with open(cache_dir / "processed_pairs.json", 'r') as f: self.processed_pairs = json.load(f) logger.info(f"Cache loaded successfully: {len(self.processed_pairs)} pairs") return True except Exception as e: logger.error(f"Error loading cache: {e}") return False def _adjust_batch_size(self) -> None: """Dynamically adjust batch size based on GPU memory usage.""" if self.memory_monitor: if self.memory_monitor.should_reduce_batch_size(): new_size = max(self.min_batch_size, self.current_batch_size // 2) if new_size != self.current_batch_size: if new_size < self.min_batch_size: logger.info(f"Reducing batch size to {new_size} due to high memory usage") self.current_batch_size = new_size gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() elif self.memory_monitor.can_increase_batch_size(): new_size = min(self.max_batch_size, int(self.current_batch_size * self.batch_increase_factor)) # More gradual increase if new_size != self.current_batch_size: if new_size > self.max_batch_size: logger.info(f"Increasing batch size to {new_size}") self.current_batch_size = new_size def _add_progress_metrics(self, pbar, **metrics) -> None: """Add memory and batch size metrics to progress bars.""" if self.memory_monitor: gpu_usage = self.memory_monitor.get_memory_usage() metrics['gpu_mem'] = f"{gpu_usage:.1%}" metrics['batch_size'] = self.current_batch_size pbar.set_postfix(**metrics) def preprocess_dialogues(self, dialogues: List[dict]) -> None: """Preprocess all dialogues with error recovery and caching.""" retry_count = 0 while retry_count < self.max_retries: try: self._preprocess_dialogues_internal(dialogues) break except Exception as e: retry_count += 1 logger.warning(f"Preprocessing attempt {retry_count} failed: {e}") if retry_count == self.max_retries: logger.error("Max retries reached. Falling back to CPU processing") self._fallback_to_cpu_processing(dialogues) def _preprocess_dialogues_internal(self, dialogues: List[dict]) -> None: """Internal preprocessing implementation with progress tracking.""" logger.info("Starting dialogue preprocessing...") # Collect unique queries and pairs unique_queries = set() query_positive_pairs = [] with tqdm(total=len(dialogues), desc="Collecting dialogue pairs") as pbar: for dialogue in dialogues: pairs = self._extract_pairs_from_dialogue(dialogue) for query, positive in pairs: unique_queries.add(query) query_positive_pairs.append((query, positive)) pbar.update(1) self._add_progress_metrics(pbar, pairs=len(query_positive_pairs)) # Precompute embeddings logger.info("Precomputing query embeddings...") self.precompute_query_embeddings(list(unique_queries)) # Find hard negatives logger.info("Finding hard negatives for all pairs...") self._find_hard_negatives_for_pairs(query_positive_pairs) def precompute_query_embeddings(self, queries: List[str]) -> None: """Precompute embeddings for all unique queries in batches.""" unique_queries = list(set(queries)) with tqdm(total=len(unique_queries), desc="Precomputing query embeddings") as pbar: for i in range(0, len(unique_queries), self.embedding_batch_size): # Adjust batch size based on memory self._adjust_batch_size() batch_size = min(self.embedding_batch_size, len(unique_queries) - i) # Get batch of queries batch_queries = unique_queries[i:i + batch_size] try: # Tokenize batch encoded = self.tokenizer( batch_queries, padding=True, truncation=True, max_length=self.max_length, return_tensors='tf' ) # Get embeddings embeddings = self.encoder(encoded['input_ids'], training=False) embeddings_np = embeddings.numpy().astype('float32') # Normalize for similarity search faiss.normalize_L2(embeddings_np) # Cache embeddings for query, emb in zip(batch_queries, embeddings_np): self.query_embeddings_cache[query] = emb pbar.update(len(batch_queries)) self._add_progress_metrics( pbar, cached=len(self.query_embeddings_cache), batch_size=batch_size ) except Exception as e: logger.warning(f"Error processing batch: {e}") # Reduce batch size and retry self.embedding_batch_size = max(self.min_batch_size, self.embedding_batch_size // 2) continue # Memory cleanup after successful batch if i % (self.embedding_batch_size * 10) == 0: gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() logger.info(f"Cached embeddings for {len(self.query_embeddings_cache)} unique queries") def _extract_pairs_from_dialogue(self, dialogue: dict) -> List[Tuple[str, str]]: """Extract query-response pairs from a dialogue.""" pairs = [] turns = dialogue.get('turns', []) for i in range(len(turns) - 1): current_turn = turns[i] next_turn = turns[i+1] if (current_turn.get('speaker') == 'user' and next_turn.get('speaker') == 'assistant' and 'text' in current_turn and 'text' in next_turn): query = current_turn['text'].strip() positive = next_turn['text'].strip() pairs.append((query, positive)) return pairs def _fallback_to_cpu_processing(self, dialogues: List[dict]) -> None: """Fallback processing method using CPU only.""" logger.info("Falling back to CPU-only processing") # Reset GPU-specific settings self.current_batch_size = self.min_batch_size self.embedding_batch_size = 32 self.search_batch_size = 16 # Attempt preprocessing with reduced batches self._preprocess_dialogues_internal(dialogues) def process_dialogues(self, dialogues: List[dict]) -> Generator[Tuple[tf.Tensor, tf.Tensor, Optional[tf.Tensor]], None, None]: """ Process dialogues using cached data with dynamic batch sizing. Yields (q_tokens['input_ids'], p_tokens['input_ids'], attention_mask) tuples. """ # Preprocess if not already done if not self.processed_pairs: self.preprocess_dialogues(dialogues) # Generate batches from cached data current_queries = [] current_positives = [] # Counters for logging total_examples_yielded = 0 total_batches_yielded = 0 with tqdm(total=len(self.processed_pairs), desc="Generating training batches", leave=False) as pbar: for i, (query, positive) in enumerate(self.processed_pairs): # Periodically adjust batch size if i % 10 == 0: # Check more frequently (e.g., every 10 pairs) self._adjust_batch_size() # Add original pair current_queries.append(query) current_positives.append(positive) # Add cached hard negatives for each query hard_negatives = self.hard_negatives_cache.get((query, positive), []) for neg_text in hard_negatives: current_queries.append(query) current_positives.append(neg_text) # If we have enough examples to form a full batch, yield it while len(current_queries) >= self.current_batch_size: batch_queries = current_queries[:self.current_batch_size] batch_positives = current_positives[:self.current_batch_size] # Update counters and logs batch_size_to_yield = len(batch_queries) total_examples_yielded += batch_size_to_yield total_batches_yielded += 1 yield self._prepare_batch(batch_queries, batch_positives, pad_to_batch_size=False) # Remove used entries current_queries = current_queries[self.current_batch_size:] current_positives = current_positives[self.current_batch_size:] # Update progress bar pbar.update(1) self._add_progress_metrics( pbar, pairs_processed=pbar.n, pending_pairs=len(current_queries) ) # After the loop, if anything is left, yield a final partial batch if current_queries: leftover_size = len(current_queries) total_examples_yielded += leftover_size total_batches_yielded += 1 yield self._prepare_batch( current_queries, current_positives, pad_to_batch_size=True ) def _find_hard_negatives_for_pairs(self, query_positive_pairs: List[Tuple[str, str]]) -> None: """Process pairs in batches to find hard negatives with GPU acceleration.""" total_pairs = len(query_positive_pairs) # Use smaller batch size for small datasets if len(self.response_pool) < 1000: batch_size = min(8, self.search_batch_size) else: batch_size = self.search_batch_size try: pbar = tqdm(total=total_pairs, desc="Finding hard negatives") is_tqdm = True except ImportError: pbar = None is_tqdm = False logger.info("Progress bar disabled - continuing without visual progress") for i in range(0, total_pairs, batch_size): self._adjust_batch_size() batch_pairs = query_positive_pairs[i:i + batch_size] batch_queries, batch_positives = zip(*batch_pairs) batch_negatives = self._find_hard_negatives_batch( list(batch_queries), list(batch_positives) ) for query, positive, negatives in zip(batch_queries, batch_positives, batch_negatives): self.hard_negatives_cache[(query, positive)] = negatives self.processed_pairs.append((query, positive)) if is_tqdm: pbar.update(len(batch_pairs)) self._add_progress_metrics( pbar, cached=len(self.processed_pairs), progress=f"{i+len(batch_pairs)}/{total_pairs}" ) if is_tqdm: pbar.close() def _find_hard_negatives_batch(self, queries: List[str], positives: List[str]) -> List[List[str]]: """Find hard negatives for a batch of queries with error handling and retries.""" retry_count = 0 total_responses = len(self.response_pool) # For very small datasets (testing), just use random sampling if total_responses < 100: all_negatives = [] for positive in positives: available = [r for r in self.response_pool if r.strip() != positive.strip()] if available: negatives = list(np.random.choice( available, size=min(self.neg_samples, len(available)), replace=False )) else: negatives = [] # Pad with empty strings if needed while len(negatives) < self.neg_samples: negatives.append("") all_negatives.append(negatives) return all_negatives while retry_count < self.max_retries: try: # Get cached embeddings and ensure they're the right type query_embeddings = np.vstack([ self.query_embeddings_cache[q] for q in queries ]).astype(np.float32) if not query_embeddings.flags['C_CONTIGUOUS']: query_embeddings = np.ascontiguousarray(query_embeddings) # Normalize embeddings faiss.normalize_L2(query_embeddings) k = 1 #min(total_responses - 1, max(3, self.neg_samples + 2)) #logger.debug(f"Searching with k={k} among {total_responses} responses") assert query_embeddings.dtype == np.float32, f"Embeddings are not float32: {query_embeddings.dtype}" # Assertion here try: distances, indices = self.index.search(query_embeddings, k) except RuntimeError as e: logger.error(f"FAISS search failed: {e}") return self._fallback_random_negatives(queries, positives) # Process results all_negatives = [] for i, (query_indices, query, positive) in enumerate(zip(indices, queries, positives)): negatives = [] positive_strip = positive.strip() # Filter valid indices and deduplicate seen = {positive_strip} for idx in query_indices: if idx >= 0 and idx < total_responses: candidate = self.response_pool[idx].strip() if candidate and candidate not in seen: # Check for non-empty strings seen.add(candidate) negatives.append(candidate) if len(negatives) >= self.neg_samples: break # If we don't have enough negatives, use random sampling from remaining pool if len(negatives) < self.neg_samples: available = [r for r in self.response_pool if r.strip() not in seen and r.strip()] if available: additional = np.random.choice( available, size=min(self.neg_samples - len(negatives), len(available)), replace=False ) negatives.extend(additional) # Still pad with empty strings if needed while len(negatives) < self.neg_samples: negatives.append("") all_negatives.append(negatives) return all_negatives except Exception as e: retry_count += 1 logger.warning(f"Hard negative search attempt {retry_count} failed: {e}") if retry_count == self.max_retries: logger.error("Max retries reached for hard negative search") return [[] for _ in queries] # Return empty lists on complete failure gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() def _fallback_random_negatives(self, queries: List[str], positives: List[str]) -> List[List[str]]: """Fallback to random sampling when similarity search fails.""" logger.warning("Falling back to random negative sampling") all_negatives = [] for positive in positives: available = [r for r in self.response_pool if r.strip() != positive.strip()] negatives = list(np.random.choice( available, size=min(self.neg_samples, len(available)), replace=False )) if available else [] while len(negatives) < self.neg_samples: negatives.append("") all_negatives.append(negatives) return all_negatives def _prepare_batch( self, queries: List[str], positives: List[str], pad_to_batch_size: bool = False ) -> Tuple[tf.Tensor, tf.Tensor, Optional[tf.Tensor]]: """Prepare a batch with dynamic padding and memory optimization.""" actual_size = len(queries) # Handle padding if requested and needed if pad_to_batch_size and actual_size < self.current_batch_size: padding_needed = self.current_batch_size - actual_size queries.extend([queries[0]] * padding_needed) positives.extend([positives[0]] * padding_needed) # Create attention mask for padded examples attention_mask = tf.concat([ tf.ones((actual_size,), dtype=tf.float32), tf.zeros((padding_needed,), dtype=tf.float32) ], axis=0) else: attention_mask = None try: # Tokenize batch q_tokens = self.tokenizer( queries, padding='max_length', truncation=True, max_length=self.max_length, return_tensors='tf' ) p_tokens = self.tokenizer( positives, padding='max_length', truncation=True, max_length=self.max_length, return_tensors='tf' ) return q_tokens['input_ids'], p_tokens['input_ids'], attention_mask except Exception as e: logger.error(f"Error preparing batch: {e}") # Emergency memory cleanup gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session() raise def estimate_total_pairs(self, dialogues: List[dict]) -> int: """Estimate total number of training pairs including hard negatives.""" base_pairs = sum( len([ 1 for i in range(len(d.get('turns', [])) - 1) if (d['turns'][i].get('speaker') == 'user' and d['turns'][i+1].get('speaker') == 'assistant') ]) for d in dialogues ) # Account for hard negatives return base_pairs * (1 + self.neg_samples) def cleanup(self): """Cleanup resources and memory.""" self.query_embeddings_cache.clear() gc.collect() if tf.config.list_physical_devices('GPU'): tf.keras.backend.clear_session()