import os import numpy as np from transformers import TFAutoModel, AutoTokenizer import tensorflow as tf from typing import 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 re from tf_data_pipeline import TFDataPipeline from response_quality_checker import ResponseQualityChecker from cross_encoder_reranker import CrossEncoderReranker from conversation_summarizer import DeviceAwareModel, Summarizer 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: """RetrievalChatbot Config""" max_context_token_limit: int = 512 embedding_dim: int = 768 encoder_units: int = 256 num_attention_heads: int = 8 dropout_rate: float = 0.2 l2_reg_weight: float = 0.001 learning_rate: float = 0.0005 min_text_length: int = 3 max_context_turns: int = 20 warmup_steps: int = 200 pretrained_model: str = 'distilbert-base-uncased' cross_encoder_model: str = 'cross-encoder/ms-marco-MiniLM-L-12-v2' summarizer_model: str = 't5-small' dtype: str = 'float32' freeze_embeddings: bool = False embedding_batch_size: int = 64 search_batch_size: int = 64 max_batch_size: int = 64 max_retries: int = 3 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 DistilBERT embeddings.""" def __init__( self, config: ChatbotConfig, name: str = "encoder", **kwargs ): super().__init__(name=name, **kwargs) self.config = config # Load pretrained model and freeze layers based on config self.pretrained = TFAutoModel.from_pretrained(config.pretrained_model) self._freeze_layers() # Add Global Average Pooling, Projection, Dropout, and Normalization layers self.pooler = tf.keras.layers.GlobalAveragePooling1D() self.projection = tf.keras.layers.Dense( config.embedding_dim, activation='tanh', name="projection", dtype=tf.float32 ) self.dropout = tf.keras.layers.Dropout(config.dropout_rate) self.normalize = tf.keras.layers.Lambda( lambda x: tf.nn.l2_normalize(x, axis=1), name="l2_normalize" ) def _freeze_layers(self): """Freeze n layers of the pretrained model""" if self.config.freeze_embeddings: self.pretrained.trainable = False logger.info("All pretrained layers frozen.") else: # Freeze only the first 'n' transformer layers for i, layer in enumerate(self.pretrained.layers): if isinstance(layer, tf.keras.layers.Layer): if hasattr(layer, 'trainable'): if i < 1: layer.trainable = False logger.info(f"Layer {i} frozen.") else: layer.trainable = True logger.info(f"Layer {i} trainable.") 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, 768] x = self.dropout(x, training=training) x = self.normalize(x) # Shape: [batch_size, 768] return x def get_config(self) -> dict: """Return the model config""" config = super().get_config() config.update({ "config": self.config.to_dict(), "name": self.name }) return config class RetrievalChatbot(DeviceAwareModel): """ Retrieval-based learning chatbot model. Uses trained embeddings and FAISS for similarity search. """ def __init__( self, config: ChatbotConfig, device: str = None, strategy=None, reranker: Optional[CrossEncoderReranker] = None, summarizer: Optional[Summarizer] = None, mode: str = 'training' ): super().__init__() self.config = config self.strategy = strategy self.device = device or self._setup_default_device() self.mode = mode.lower() # Initialize reranker, summarizer, tokenizer, and encoder self.reranker = reranker or self._initialize_reranker() self.tokenizer = self._initialize_tokenizer() self.encoder = self._initialize_encoder() self.summarizer = summarizer or self._initialize_summarizer() # Initialize data pipeline logger.info("Initializing TFDataPipeline.") self.data_pipeline = TFDataPipeline( config=self.config, tokenizer=self.tokenizer, encoder=self.encoder, response_pool=[], max_length=self.config.max_context_token_limit, query_embeddings_cache={}, ) # Collect unique responses from dialogues if self.mode == 'inference': logger.info("Mode set to 'inference'. Loading FAISS index and response pool.") self._load_faiss_index_and_responses() elif self.mode != 'training': logger.error(f"Unsupported mode in RetrievalChatbot init: {self.mode}") raise ValueError(f"Unsupported mode in RetrievalChatbot init: {self.mode}") # Initialize training history self.history = { "train_loss": [], "val_loss": [], "train_metrics": {}, "val_metrics": {} } def _setup_default_device(self) -> str: """Set up default device if none is provided.""" if tf.config.list_physical_devices('GPU'): return 'GPU' else: return 'CPU' def _initialize_reranker(self) -> CrossEncoderReranker: """Initialize the CrossEncoderReranker.""" logger.info("Initializing default CrossEncoderReranker...") return CrossEncoderReranker(model_name=self.config.cross_encoder_model) def _initialize_summarizer(self) -> Summarizer: """Initialize the Summarizer.""" return Summarizer( tokenizer=self.tokenizer, model_name=self.config.summarizer_model, max_summary_length=self.config.max_context_token_limit // 4, device=self.device, max_summary_rounds=2 ) def _initialize_tokenizer(self) -> AutoTokenizer: """Initialize the tokenizer and add special tokens.""" logger.info("Initializing tokenizer and adding special tokens...") tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_model) special_tokens = { "user": "", "assistant": "", "context": "", "sep": "" } tokenizer.add_special_tokens( {'additional_special_tokens': list(special_tokens.values())} ) return tokenizer def _initialize_encoder(self) -> EncoderModel: """Initialize the EncoderModel and resize token embeddings.""" logger.info("Initializing encoder model...") encoder = EncoderModel( self.config, name="shared_encoder", ) new_vocab_size = len(self.tokenizer) encoder.pretrained.resize_token_embeddings(new_vocab_size) logger.info(f"Token embeddings resized to: {new_vocab_size}") return encoder def _load_faiss_index_and_responses(self) -> None: """Load FAISS index and response pool for inference.""" try: logger.info(f"Loading FAISS index from {self.data_pipeline.faiss_index_file_path}...") self.data_pipeline.load_faiss_index(self.data_pipeline.faiss_index_file_path) logger.info("FAISS index loaded successfully.") # Load response pool response_pool_path = self.data_pipeline.faiss_index_file_path.replace('.index', '_responses.json') if os.path.exists(response_pool_path): with open(response_pool_path, 'r', encoding='utf-8') as f: self.data_pipeline.response_pool = json.load(f) logger.info(f"Loaded {len(self.data_pipeline.response_pool)} responses from {response_pool_path}.") else: logger.error(f"Response pool file not found at {response_pool_path}.") raise FileNotFoundError(f"Response pool file not found at {response_pool_path}.") # Validate FAISS index and response pool self.data_pipeline.validate_faiss_index() logger.info("FAISS index and response pool validated successfully.") except Exception as e: logger.error(f"Failed to load FAISS index and response pool: {e}") raise @classmethod def load_model(cls, load_dir: Union[str, Path], mode: str = 'training') -> '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, mode=mode) # Load DistilBERT chatbot.encoder.pretrained = TFAutoModel.from_pretrained(load_dir / "shared_encoder", config=config) dummy_input = tf.zeros((1, config.max_context_token_limit), dtype=tf.int32) _ = chatbot.encoder(dummy_input, training=False) # Load tokenizer chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer") logger.info(f"Models and tokenizer loaded from {load_dir}") # Load the custom weights custom_weights_path = load_dir / "encoder_custom_weights.weights.h5" if custom_weights_path.exists(): chatbot.encoder.load_weights(str(custom_weights_path)) logger.info("Loaded custom encoder weights for projection/dropout/etc.") else: logger.warning(f"No custom encoder weights found at {custom_weights_path}. The top-level projection layer won't have learned parameters.") # Handle 'inference' mode: load FAISS, etc. if mode == 'inference': cls._prepare_model_for_inference(chatbot, load_dir) return chatbot @classmethod def _prepare_model_for_inference(cls, chatbot: 'RetrievalChatbot', load_dir: Path) -> None: """Load inference components.""" try: # Load FAISS index faiss_path = load_dir / 'faiss_indices/faiss_index_production.index' if faiss_path.exists(): chatbot.index = faiss.read_index(str(faiss_path)) logger.info("FAISS index loaded successfully") else: raise FileNotFoundError(f"FAISS index not found at {faiss_path}") # Load response pool response_pool_path = load_dir / 'faiss_indices/faiss_index_production_responses.json' if response_pool_path.exists(): with open(response_pool_path, 'r') as f: chatbot.response_pool = json.load(f) logger.info(f"Loaded {len(chatbot.response_pool)} responses") else: raise FileNotFoundError(f"Response pool not found at {response_pool_path}") # Verify dimensions match if chatbot.index.d != chatbot.config.embedding_dim: raise ValueError( f"FAISS index dimension {chatbot.index.d} doesn't match " f"model dimension {chatbot.config.embedding_dim}" ) except Exception as e: logger.error(f"Error loading inference components: {e}") raise def save_models(self, save_dir: Union[str, Path]): """Save model and config""" 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 the HF DistilBERT submodule, custom top-level layers, and tokenizer self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder") self.encoder.save_weights(save_dir / "encoder_custom_weights.weights.h5") self.tokenizer.save_pretrained(save_dir / "tokenizer") logger.info(f"Models and tokenizer saved to {save_dir}.") def retrieve_responses( self, query: str, top_k: int = 10, reranker: Optional[CrossEncoderReranker] = None, summarizer: Optional[Summarizer] = None, summarize_threshold: int = 512 ) -> List[Tuple[str, float]]: """ Retrieve top-k responses using FAISS and cross-encoder re-ranking. Args: query: The user's input text. top_k: Number of FAISS results to return reranker: CrossEncoderReranker for refined scoring summarizer: Summarizer for long queries summarize_threshold: Summarize if conversation tokens > threshold. Returns: List of (response_text, final_score). """ def sigmoid(x: float) -> float: return 1 / (1 + np.exp(-x)) # Query summarization if summarizer and len(query.split()) > summarize_threshold: logger.info(f"Query is long ({len(query.split())} words). Summarizing.") query = summarizer.summarize_text(query) logger.info(f"Summarized Query: {query}") detected_domain = self.detect_domain_from_query(query) # Retrieve initial candidates from FAISS initial_k = min(top_k * 10, len(self.data_pipeline.response_pool)) faiss_candidates = self.faiss_search(query, domain=detected_domain, top_k=initial_k) if not faiss_candidates: return [] texts = [item[0] for item in faiss_candidates] if not reranker: reranker = CrossEncoderReranker(model_name=self.config.cross_encoder_model) # Re-rank the texts (candidates) from FAISS search using the cross-encoder ce_logits = reranker.rerank(query, texts, max_length=256) # Combine scores from FAISS and cross-encoder final_candidates = [] for (resp_text, faiss_score), logit in zip(faiss_candidates, ce_logits): ce_prob = sigmoid(logit) # now in range [0...1] faiss_norm = (faiss_score + 1)/2.0 # now in range [0...1] combined_score = 0.85 * ce_prob + 0.15 * faiss_norm length_adjusted_score = self.length_adjust_score(resp_text, combined_score) final_candidates.append((resp_text, length_adjusted_score)) # Sort descending by combined score final_candidates.sort(key=lambda x: x[1], reverse=True) # Return top_k return final_candidates[:top_k] def extract_keywords(self, query: str) -> List[str]: """ Return any domain keywords present in the query (lowercased). """ domain_keywords = { 'restaurant': ['restaurant', 'dining', 'food', 'dine', 'reservation', 'table', 'menu', 'cuisine', 'eat', 'place to eat', 'hungry', 'chef', 'dish', 'meal', 'brunch', 'bistro', 'buffet', 'catering', 'gourmet', 'fast food', 'fine dining', 'takeaway', 'delivery', 'restaurant booking'], 'movie': ['movie', 'cinema', 'film', 'ticket', 'showtime', 'showing', 'theater', 'flick', 'screening', 'film ticket', 'film show', 'blockbuster', 'premiere', 'trailer', 'director', 'actor', 'actress', 'plot', 'genre', 'screen', 'sequel', 'animation', 'documentary'], 'ride_share': ['ride', 'taxi', 'uber', 'lyft', 'car service', 'pickup', 'dropoff', 'driver', 'cab', 'hailing', 'rideshare', 'ride hailing', 'carpool', 'chauffeur', 'transit', 'transportation', 'hail ride'], 'coffee': ['coffee', 'café', 'cafe', 'starbucks', 'espresso', 'latte', 'mocha', 'americano', 'barista', 'brew', 'cappuccino', 'macchiato', 'iced coffee', 'cold brew', 'espresso machine', 'coffee shop', 'tea', 'chai', 'java', 'bean', 'roast', 'decaf'], 'pizza': ['pizza', 'delivery', 'order food', 'pepperoni', 'topping', 'pizzeria', 'slice', 'pie', 'margherita', 'deep dish', 'thin crust', 'cheese', 'oven', 'tossed', 'sauce', 'garlic bread', 'calzone'], 'auto': ['car', 'vehicle', 'repair', 'maintenance', 'mechanic', 'oil change', 'garage', 'auto shop', 'tire', 'check engine', 'battery', 'transmission', 'brake', 'engine diagnostics', 'carwash', 'detail', 'alignment', 'exhaust', 'spark plug', 'dashboard'], } query_lower = query.lower() found = set() for domain, kw_list in domain_keywords.items(): for kw in kw_list: if kw in query_lower: found.add(kw) return list(found) def length_adjust_score(self, text: str, base_score: float) -> float: """ Penalize very short lines, reward longer lines. """ words = text.split() wcount = len(words) # Penalty if under 4 words if wcount < 4: return base_score * 0.8 # Bonus for lines > 15 words if wcount > 15: bonus = min(0.03, 0.001 * (wcount - 15)) base_score += bonus return base_score def detect_domain_from_query(self, query: str) -> str: """ Detect the domain of the query based on keywords. Used for boosting FAISS search. """ domain_patterns = { 'restaurant': r'\b(restaurant|restaurants?|dining|food|foods?|dine|reservation|reservations?|table|tables?|menu|menus?|cuisine|cuisines?|eat|eats?|place\s?to\s?eat|places\s?to\s?eat|hungry|chef|chefs?|dish|dishes?|meal|meals?|fork|forks?|knife|knives?|spoon|spoons?|brunch|bistro|buffet|buffets?|catering|caterings?|gourmet|fast\s?food|fine\s?dining|takeaway|takeaways?|delivery|deliveries|restaurant\s?booking)\b', 'movie': r'\b(movie|movies?|cinema|cinemas?|film|films?|ticket|tickets?|showtime|showtimes?|showing|showings?|theater|theaters?|flick|flicks?|screening|screenings?|film\s?ticket|film\s?tickets?|film\s?show|film\s?shows?|blockbuster|blockbusters?|premiere|premieres?|trailer|trailers?|director|directors?|actor|actors?|actress|actresses?|plot|plots?|genre|genres?|screen|screens?|sequel|sequels?|animation|animations?|documentary|documentaries)\b', 'ride_share': r'\b(ride|rides?|taxi|taxis?|uber|lyft|car\s?service|car\s?services?|pickup|pickups?|dropoff|dropoffs?|driver|drivers?|cab|cabs?|hailing|hailings?|rideshare|rideshares?|ride\s?hailing|ride\s?hailings?|carpool|carpools?|chauffeur|chauffeurs?|transit|transits?|transportation|transportations?|hail\s?ride|hail\s?rides?)\b', 'coffee': r'\b(coffee|coffees?|café|cafés?|cafe|cafes?|starbucks|espresso|espressos?|latte|lattes?|mocha|mochas?|americano|americanos?|barista|baristas?|brew|brews?|cappuccino|cappuccinos?|macchiato|macchiatos?|iced\s?coffee|iced\s?coffees?|cold\s?brew|cold\s?brews?|espresso\s?machine|espresso\s?machines?|coffee\s?shop|coffee\s?shops?|tea|teas?|chai|chais?|java|javas?|bean|beans?|roast|roasts?|decaf)\b', 'pizza': r'\b(pizza|pizzas?|delivery|deliveries|order\s?food|order\s?foods?|pepperoni|pepperonis?|topping|toppings?|pizzeria|pizzerias?|slice|slices?|pie|pies?|margherita|margheritas?|deep\s?dish|deep\s?dishes?|thin\s?crust|thin\s?crusts?|cheese|cheeses?|oven|ovens?|tossed|tosses?|sauce|sauces?|garlic\s?bread|garlic\s?breads?|calzone|calzones?)\b', 'auto': r'\b(car|cars?|vehicle|vehicles?|repair|repairs?|maintenance|maintenances?|mechanic|mechanics?|oil\s?change|oil\s?changes?|garage|garages?|auto\s?shop|auto\s?shops?|tire|tires?|check\s?engine|check\s?engines?|battery|batteries?|transmission|transmissions?|brake|brakes?|engine\s?diagnostics|engine\s?diagnostic|carwash|carwashes?|detail|details?|alignment|alignments?|exhaust|exhausts?|spark\s?plug|spark\s?plugs?|dashboard|dashboards?)\b', } # Check for matches for domain, pattern in domain_patterns.items(): if re.search(pattern, query.lower()): return domain return 'other' def is_numeric_response(self, text: str) -> bool: """ Return True if `text` is purely digits and/or spaces. """ pattern = r'^[\s]*[\d]+([\s.,\d]+)*[\s]*$' return bool(re.match(pattern, text.strip())) def faiss_search( self, query: str, domain: str = 'other', top_k: int = 10, boost_factor: float = 1.15 ) -> List[Tuple[str, float]]: """ Retrieve top-k responses from the FAISS index (IndexFlatIP) given a user query. Args: query (str): The user input text. domain (str): The detected domain from possible domains: ['restaurant', 'movie', 'ride_share', 'coffee', 'pizza', 'auto', 'other'] top_k (int): Number of top results to return. boost_factor (float, optional): Factor to boost scores for keyword matches. Returns: List[Tuple[str, float]]: List of (response_text, similarity) sorted by descending similarity. """ # Encode the query q_emb = self.data_pipeline.encode_query(query) q_emb_np = q_emb.reshape(1, -1).astype('float32') # Search the index distances, indices = self.data_pipeline.index.search(q_emb_np, top_k * 10) # IndexFlatIP: 'distances' are inner products (cosine similarities for normalized vectors). candidates = [] for rank, idx in enumerate(indices[0]): if idx < 0: continue text_dict = self.data_pipeline.response_pool[idx] text = text_dict.get('text', '').strip() cand_domain = text_dict.get('domain', 'other') score = distances[0][rank] # Skip purely numeric or extremely short text (fewer than 3 words): words = text.split() if len(words) < 4: continue if self.is_numeric_response(text): continue candidates.append((text, cand_domain, score)) if not candidates: logger.warning("No valid candidates found after initial numeric/length filtering.") return [] # Sort candidates by score descending candidates.sort(key=lambda x: x[2], reverse=True) # Filter in-domain responses in_domain = [c for c in candidates if c[1] == domain] if not in_domain: logger.info(f"No in-domain responses found for '{domain}'. Using all candidates.") in_domain = candidates # Boost responses containing query keywords query_keywords = self.extract_keywords(query) boosted = [] for (resp_text, resp_domain, score) in in_domain: new_score = score # If the domain is known AND the response text shares any query keywords, boost it if query_keywords and any(kw in resp_text.lower() for kw in query_keywords): new_score *= boost_factor # Apply length penalty/bonus new_score = self.length_adjust_score(resp_text, new_score) boosted.append((resp_text, new_score)) # Sort boosted responses boosted.sort(key=lambda x: x[1], reverse=True) # Debug logging (see FAISS responses) # for resp, score in boosted[:100]: # logger.debug(f"Candidate: '{resp}' with score {score}") return boosted[:top_k] def chat( self, query: str, conversation_history: Optional[List[Tuple[str, str]]] = None, quality_checker: Optional['ResponseQualityChecker'] = None, top_k: int = 10, ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]: """ Live chat with the chatbot. Uses same processing flow as validation, except for context handling and quality checking. """ @self.run_on_device def get_response(self_arg, query_arg): # Build conversation context string conversation_str = self_arg._build_conversation_context(query_arg, conversation_history) # Retrieve and re-rank results = self_arg.retrieve_responses( query=conversation_str, top_k=top_k, reranker=self_arg.reranker, summarizer=self_arg.summarizer, summarize_threshold=512 ) # Handle low confidence or empty responses if not results: return ("I'm sorry, but I couldn't find a relevant response.", [], {}) 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 get_response(self, query) def _build_conversation_context( self, query: str, conversation_history: Optional[List[Tuple[str, str]]] ) -> str: """ Build conversation context string from conversation history. """ if not conversation_history: return f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('')]} {query}" conversation_parts = [] for user_txt, assistant_txt in conversation_history: conversation_parts.extend([ f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('')]} {user_txt}", f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('')]} {assistant_txt}" ]) conversation_parts.append(f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('')]} {query}") return "\n".join(conversation_parts) def train_model( self, tfrecord_file_path: str, epochs: int = 20, batch_size: int = 16, validation_split: float = 0.2, checkpoint_dir: str = "checkpoints/", use_lr_schedule: bool = True, peak_lr: float = 1e-5, warmup_steps_ratio: float = 0.1, early_stopping_patience: int = 3, min_delta: float = 1e-4, test_mode: bool = False, initial_epoch: int = 0 ) -> None: """ Train the retrieval model using a pre-prepared TFRecord dataset. - Checkpoint loading/restoring - LR scheduling - Epoch/iteration tracking - Training-history logging - Early stopping - Custom loss function (Contrastive loss with hard negative sampling)) """ logger.info("Starting training with pre-prepared TFRecord dataset...") def parse_tfrecord_fn(example_proto, max_length, neg_samples): """ Parses a single TFRecord example. """ feature_description = { 'query_ids': tf.io.FixedLenFeature([max_length], tf.int64), 'positive_ids': tf.io.FixedLenFeature([max_length], tf.int64), 'negative_ids': tf.io.FixedLenFeature([neg_samples * max_length], tf.int64), } parsed_features = tf.io.parse_single_example(example_proto, feature_description) query_ids = tf.cast(parsed_features['query_ids'], tf.int32) positive_ids = tf.cast(parsed_features['positive_ids'], tf.int32) negative_ids = tf.cast(parsed_features['negative_ids'], tf.int32) negative_ids = tf.reshape(negative_ids, [neg_samples, max_length]) return query_ids, positive_ids, negative_ids # Count total records in TFRecord raw_dataset = tf.data.TFRecordDataset(tfrecord_file_path) total_pairs = sum(1 for _ in raw_dataset) logger.info(f"Total pairs in TFRecord: {total_pairs}") train_size = int(total_pairs * (1 - validation_split)) val_size = total_pairs - train_size steps_per_epoch = math.ceil(train_size / batch_size) val_steps = math.ceil(val_size / batch_size) total_steps = steps_per_epoch * epochs buffer_size = max(1, total_pairs // 2) # 50% of the dataset for shuffling logger.info(f"Training pairs: {train_size}") logger.info(f"Validation pairs: {val_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 & LR 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=tf.cast(peak_lr, tf.float32), 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=tf.cast(peak_lr, tf.float32)) logger.info("Using fixed learning rate.") # Dummy step to force initialization dummy_input = tf.zeros((1, self.config.max_context_token_limit), dtype=tf.int32) with tf.GradientTape() as tape: dummy_output = self.encoder(dummy_input) dummy_loss = tf.cast(tf.reduce_mean(dummy_output), tf.float32) dummy_grads = tape.gradient(dummy_loss, self.encoder.trainable_variables) self.optimizer.apply_gradients(zip(dummy_grads, self.encoder.trainable_variables)) # Create checkpoint and manager checkpoint = tf.train.Checkpoint( epoch=tf.Variable(0, dtype=tf.int32), optimizer=self.optimizer, model=self.encoder ) # Create a CheckpointManager manager = tf.train.CheckpointManager( checkpoint, directory=checkpoint_dir, max_to_keep=3, checkpoint_name='ckpt' ) # Restore from existing checkpoint if one is provided latest_checkpoint = manager.latest_checkpoint history_path = Path(checkpoint_dir) / 'training_history.json' # Log epoch losses across runs, including restore from checkpoint if not hasattr(self, 'history'): self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []} if latest_checkpoint and not test_mode: # Debug checkpoint loading # logger.info(f"\nTrying to load checkpoint from: {latest_checkpoint}") # reader = tf.train.load_checkpoint(latest_checkpoint) # shape_from_key = reader.get_variable_to_shape_map() # dtype_from_key = reader.get_variable_to_dtype_map() # logger.info("\nCheckpoint Variables:") # for key in shape_from_key: # logger.info(f"{key}: dtype={dtype_from_key[key]} - Shape: {shape_from_key[key]}") status = checkpoint.restore(latest_checkpoint) status.assert_consumed() logger.info(f"Restored from checkpoint: {latest_checkpoint}") logger.info(f"Optimizer iterations after restore: {self.optimizer.iterations.numpy()}") # Verify learning rate after restore if use_lr_schedule: current_lr = float(lr_schedule(self.optimizer.iterations)) else: current_lr = float(self.optimizer.learning_rate.numpy()) logger.info(f"Current learning rate after restore: {current_lr:.2e}") # Derive initial_epoch from checkpoint name if not passed in ckpt_number = int(latest_checkpoint.split('ckpt-')[-1]) if initial_epoch == 0: initial_epoch = ckpt_number # Assign to checkpoint.epoch for counting checkpoint.epoch.assign(tf.cast(initial_epoch, tf.int32)) logger.info(f"Resuming from epoch {initial_epoch}") # Load history from file: if history_path.exists(): try: with open(history_path, 'r') as f: self.history = json.load(f) logger.info(f"Loaded previous training history from {history_path}") except Exception as e: logger.warning(f"Could not load history, starting fresh: {e}") # Save custom weights not being saved in the full model. # This was a bugfix to extract weights from a checkpoint without retraining. # Before updating save_models, only Distilbert weights were being saved (custom layers were missed). # Not needed, also not harmful. self.save_models(Path(checkpoint_dir) / "pretrained_full_model") logger.info(f"Manually saved custom weights after restore.") else: logger.info("Starting training from scratch") checkpoint.epoch.assign(tf.cast(0, tf.int32)) initial_epoch = 0 # Set up 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}") # Parse dataset dataset = tf.data.TFRecordDataset(tfrecord_file_path) # Debug mode uses small subset. Useful for CPU debugging. if test_mode: subset_size = 200 dataset = dataset.take(subset_size) logger.info(f"TEST MODE: Using only {subset_size} examples") # Recompute sizes, steps, epochs, etc., as needed total_pairs = subset_size train_size = int(total_pairs * (1 - validation_split)) val_size = total_pairs - train_size batch_size = min(batch_size, val_size) steps_per_epoch = math.ceil(train_size / batch_size) val_steps = math.ceil(val_size / batch_size) total_steps = steps_per_epoch * epochs buffer_size = max(1, total_pairs // 10) epochs = min(epochs, 5) # For quick debug early_stopping_patience = 2 logger.info(f"New training pairs: {train_size}") logger.info(f"New validation pairs: {val_size}") dataset = dataset.map( lambda x: parse_tfrecord_fn(x, self.config.max_context_token_limit, self.data_pipeline.neg_samples), num_parallel_calls=tf.data.AUTOTUNE ) # Train/val split train_dataset = dataset.take(train_size) val_dataset = dataset.skip(train_size).take(val_size) # Shuffle and batch train_dataset = train_dataset.shuffle(buffer_size=buffer_size) train_dataset = train_dataset.batch(batch_size, drop_remainder=True) train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE) val_dataset = val_dataset.batch(batch_size, drop_remainder=False) val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE) val_dataset = val_dataset.cache() # Training loop best_val_loss = float("inf") epochs_no_improve = 0 for epoch in range(int(checkpoint.epoch.numpy()) + 1, epochs + 1): checkpoint.epoch.assign(epoch) logger.info(f"Starting Epoch {epoch}...") epoch_loss_avg = tf.keras.metrics.Mean(dtype=tf.float32) batches_processed = 0 try: train_pbar = tqdm( total=steps_per_epoch, desc=f"Training Epoch {epoch}", unit="batch" ) is_tqdm_train = True except ImportError: train_pbar = None is_tqdm_train = False # --- Training --- for q_batch, p_batch, n_batch in train_dataset: loss, grad_norm, post_clip_norm = self.train_step(q_batch, p_batch, n_batch) epoch_loss_avg(loss) batches_processed += 1 # Log to TensorBoard with train_summary_writer.as_default(): step = (epoch - 1) * steps_per_epoch + batches_processed tf.summary.scalar("loss", tf.cast(loss, tf.float32), step=step) tf.summary.scalar("gradient_norm_pre_clip", tf.cast(grad_norm, tf.float32), step=step) tf.summary.scalar("gradient_norm_post_clip", tf.cast(post_clip_norm, tf.float32), step=step) # 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}", "pre_clip": f"{grad_norm.numpy():.2e}", "post_clip": f"{post_clip_norm.numpy():.2e}", "lr": f"{current_lr:.2e}", "batches": f"{batches_processed}/{steps_per_epoch}" }) gc.collect() # End the epoch early if we've processed all steps if batches_processed >= steps_per_epoch: break if is_tqdm_train and train_pbar: train_pbar.close() # --- Validation --- val_loss_avg = tf.keras.metrics.Mean(dtype=tf.float32) val_batches_processed = 0 try: val_pbar = tqdm(total=val_steps, desc="Validation", unit="batch") is_tqdm_val = True except ImportError: val_pbar = None is_tqdm_val = False last_valid_val_loss = None valid_batches = False for q_batch, p_batch, n_batch in val_dataset: # If batch is too small, skip if tf.shape(q_batch)[0] < 2: logger.warning(f"Skipping validation batch of size {tf.shape(q_batch)[0]}") continue valid_batches = True val_loss = self.validation_step(q_batch, p_batch, n_batch) val_loss_avg(val_loss) last_valid_val_loss = 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}" }) gc.collect() if val_batches_processed >= val_steps: break if not valid_batches: # If no valid batch is found, fallback logger.warning("No valid validation batches in this epoch") if last_valid_val_loss is not None: val_loss = last_valid_val_loss val_loss_avg(val_loss) else: val_loss = epoch_loss_avg.result() val_loss_avg(val_loss) if is_tqdm_val and val_pbar: val_pbar.close() # End of epoch: final 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}") # TensorBoard epoch logs with train_summary_writer.as_default(): tf.summary.scalar("epoch_loss", train_loss, step=epoch) with val_summary_writer.as_default(): tf.summary.scalar("val_loss", val_loss, step=epoch) # Save checkpoint manager.save() # Save model for iterative testing/inference model_save_path = Path(checkpoint_dir) / f"model_epoch_{epoch}" self.save_models(model_save_path) logger.info(f"Saved model for epoch {epoch} at {model_save_path}") # Update local history self.history['train_loss'].append(train_loss) self.history['val_loss'].append(val_loss) self.history.setdefault('learning_rate', []).append(current_lr) def convert_to_py_floats(obj): if isinstance(obj, dict): return {k: convert_to_py_floats(v) for k, v in obj.items()} elif isinstance(obj, list): return [convert_to_py_floats(x) for x in obj] elif isinstance(obj, (np.float32, np.float64)): return float(obj) elif tf.is_tensor(obj): return float(obj.numpy()) else: return obj json_history = convert_to_py_floats(self.history) # Save training history to file every epoch with open(history_path, 'w') as f: json.dump(json_history, f) logger.info(f"Saved training history to {history_path}") # Early stopping 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 logger.info("Training completed!") @tf.function def train_step( self, q_batch: tf.Tensor, p_batch: tf.Tensor, n_batch: tf.Tensor ) -> tf.Tensor: """ Single training step using queries, positives, and hard negatives. """ with tf.GradientTape() as tape: # Encode queries, positives, and negatives q_enc = self.encoder(q_batch, training=True) # [batch_size, embed_dim] p_enc = self.encoder(p_batch, training=True) # [batch_size, embed_dim] shape = tf.shape(n_batch) bs = shape[0] neg_samples = shape[1] # Flatten negatives to feed them in one pass: [batch_size * neg_samples, max_length] n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]]) n_enc_flat = self.encoder(n_batch_flat, training=True) # [bs*neg_samples, embed_dim] # Reshape back => [batch_size, neg_samples, embed_dim] n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1]) # Combine the positive embedding and negative embeddings along dim=1: shape [batch_size, 1 + neg_samples, embed_dim] # Col 1 is the pos, subsequent cols are negatives combined_p_n = tf.concat([tf.expand_dims(p_enc, axis=1), n_enc], axis=1) # [bs, (1+neg_samples), embed_dim] # Compute scores: dot product of q_enc with each column in combined_p_n. `tf.einsum` handles the batch dimension dot_products = tf.cast(tf.einsum('bd,bkd->bk', q_enc, combined_p_n), tf.float32) labels = tf.zeros([bs], dtype=tf.int32) # Keep labels as int32 loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=dot_products ) loss = tf.cast(tf.reduce_mean(loss), tf.float32) # Calculate gradients and clip gradients = tape.gradient(loss, self.encoder.trainable_variables) gradients_norm = tf.cast(tf.linalg.global_norm(gradients), tf.float32) max_grad_norm = tf.constant(1.5, dtype=tf.float32) gradients, _ = tf.clip_by_global_norm(gradients, max_grad_norm, gradients_norm) post_clip_norm = tf.cast(tf.linalg.global_norm(gradients), tf.float32) self.optimizer.apply_gradients(zip(gradients, self.encoder.trainable_variables)) return loss, gradients_norm, post_clip_norm @tf.function def validation_step( self, q_batch: tf.Tensor, p_batch: tf.Tensor, n_batch: tf.Tensor ) -> tf.Tensor: """ Single validation step using queries, positives, and hard negatives. Same idea as train_step, but without gradient updates. """ q_enc = self.encoder(q_batch, training=False) p_enc = self.encoder(p_batch, training=False) shape = tf.shape(n_batch) bs = shape[0] neg_samples = shape[1] n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]]) n_enc_flat = self.encoder(n_batch_flat, training=False) n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1]) combined_p_n = tf.concat( [tf.expand_dims(p_enc, axis=1), n_enc], axis=1 ) dot_products = tf.cast(tf.einsum('bd,bkd->bk', q_enc, combined_p_n), tf.float32) labels = tf.zeros([bs], dtype=tf.int32) loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=dot_products ) loss = tf.cast(tf.reduce_mean(loss), tf.float32) return loss def _get_lr_schedule( self, total_steps: int, peak_lr: float, warmup_steps: int ) -> tf.keras.optimizers.schedules.LearningRateSchedule: """ 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) # warmup_steps 10% 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 constants self.initial_lr = tf.cast(self.peak_lr * 0.1, tf.float32) self.min_lr = tf.cast(self.peak_lr * 0.01, tf.float32) 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 warmup_factor = tf.cast(tf.minimum(1.0, step / self.warmup_steps), tf.float32) warmup_lr = self.initial_lr + (self.peak_lr - self.initial_lr) * warmup_factor # Decay decay_steps = tf.cast(tf.maximum(1.0, self.total_steps - self.warmup_steps), tf.float32) decay_factor = tf.cast((step - self.warmup_steps) / decay_steps, tf.float32) decay_factor = tf.cast(tf.minimum(tf.maximum(0.0, decay_factor), 1.0), tf.float32) cosine_decay = tf.cast(0.5 * (1.0 + tf.cos(tf.constant(math.pi, dtype=tf.float32) * decay_factor)), tf.float32) decay_lr = self.min_lr + (self.peak_lr - self.min_lr) * cosine_decay final_lr = tf.where(step < self.warmup_steps, warmup_lr, decay_lr) # Ensure valid lr 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)