beam_nrpa_v00001.py

import yaml
import heapq
import argparse
import numpy as np
import language_tool_python

from functools import lru_cache
from collections import Counter
from nltk.translate.bleu_score import sentence_bleu
from nltk.tokenize import word_tokenize

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM

class BeamNRPA:
    def __init__(self, config):
        self.model, self.tokenizer = load_finetuned_llm(config['model']['path_or_name'])
        
        initial_policy = {
            'model': self.model,
            'tokenizer': self.tokenizer,
            'base_prompt': config['model']['initial_prompt'],
            'generation_params': config['generation_params'],
            'max_context_length': min(self.model.config.max_position_embeddings, 1024),
            'forbidden_words': [],
        }
        
        self.policy_adapter = PolicyAdapter(initial_policy)
        self.action_sampler = ActionSampler(self.policy_adapter.policy)
        self.scorer = Scorer(config['scorer'])
        
        self.max_level = config['beam_nrpa']['max_level']
        self.beam_width = config['beam_nrpa']['beam_width']
        self.adapt_iterations = config['beam_nrpa']['adapt_iterations']
        self.rollout_limit = config['beam_nrpa']['rollout_limit']
        
        self.best_solution = None

    def nested_beam_search(self, level):
        if level == 0:
            return [self.rollout() for _ in range(self.beam_width)]
        
        next_beam = []
        for _ in range(self.beam_width):
            sequence = []
            for _ in range(self.rollout_limit):
                action = self.action_sampler.sample_action([sequence])[0]
                sequence.append(action)
                child_beam = self.nested_beam_search(level - 1)
                best_child = max(child_beam, key=lambda x: x[1])
                sequence.extend(best_child[0])
            score = self.scorer.score(sequence)
            heapq.heappush(next_beam, (-score, sequence))
            if len(next_beam) > self.beam_width:
                heapq.heappop(next_beam)
        
        return [(-score, seq) for score, seq in next_beam]

    def rollout(self):
        sequence = []
        for _ in range(self.rollout_limit):
            action = self.action_sampler.sample_action([sequence])[0]
            sequence.append(action)
        score = self.scorer.score(sequence)
        return (sequence, score)

    def run(self):
        for _ in range(self.adapt_iterations):
            beam = self.nested_beam_search(self.max_level)
            best_in_iteration = max(beam, key=lambda x: x[0])
            
            if self.best_solution is None or best_in_iteration[0] > self.best_solution[0]:
                self.best_solution = best_in_iteration
            
            self.policy_adapter.adapt_policy(best_in_iteration[1], best_in_iteration[0])
            self.action_sampler.policy = self.policy_adapter.policy
        
        return self.best_solution
    
def load_finetuned_llm(model_path_or_name):
    """
    Load a fine-tuned language model from a local path or Hugging Face model hub.
    
    Args:
    model_path_or_name (str): Local path or Hugging Face model name
    
    Returns:
    tuple: (model, tokenizer)
    """
    try:
        print(f"Loading model from {model_path_or_name}")
        
        # Load tokenizer
        tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)
        
        # Load model
        model = AutoModelForCausalLM.from_pretrained(
            model_path_or_name,
            device_map="auto",  # Automatically choose the best device setup
            torch_dtype=torch.float16  # Use float16 for efficiency if supported
        )
        
        print(f"Model loaded successfully. Model type: {type(model).__name__}")
        
        return model, tokenizer
    
    except Exception as e:
        print(f"Error loading model: {str(e)}")
        raise

class ActionSampler:
    def __init__(self, policy):
        self.policy = policy
        self.model = policy['model']
        self.tokenizer = policy['tokenizer']
        self.cache = {}

    @lru_cache(maxsize=1000)
    def _tokenize(self, text):
        return self.tokenizer(text, return_tensors="pt")

    def _truncate_inputs(self, inputs):
        max_length = self.policy['max_context_length']
        if inputs.input_ids.shape[1] > max_length:
            inputs.input_ids = inputs.input_ids[:, -max_length:]
            inputs.attention_mask = inputs.attention_mask[:, -max_length:]
        return inputs

    def _adaptive_temperature(self, sequence):
        # Implement adaptive temperature based on sequence length or other factors
        base_temp = self.policy['generation_params']['temperature']
        return base_temp * (1 + len(sequence) * 0.01)  # Example: increase temperature slightly as sequence grows

    def _constrained_decoding(self, outputs):
        # Implement constrained decoding
        forbidden_words = self.policy.get('forbidden_words', [])
        for word in forbidden_words:
            outputs = outputs.filter(lambda x: word not in x)
        return outputs

    def sample_action(self, sequences):
        try:
            full_prompts = [self.policy['base_prompt'] + ' '.join(seq) for seq in sequences]
            inputs = self.tokenizer(full_prompts, return_tensors="pt", padding=True, truncation=True)
            
            # Move inputs to the same device as the model
            inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
            
            with torch.no_grad():
                outputs = self.model.generate(
                    **inputs,
                    do_sample=True,
                    **self.policy['generation_params'],
                    pad_token_id=self.tokenizer.eos_token_id
                )
            
            # Decode and post-process
            generated_texts = self.tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)
            generated_texts = [self.post_process(text) for text in generated_texts]
            
            return generated_texts

        except Exception as e:
            print(f"Error in sample_action: {str(e)}")
            return ["" for _ in sequences]  # Return empty strings in case of error

    def post_process(self, text):
        # Implement post-processing
        text = text.strip()
        # Add more post-processing steps as needed
        return text

class PolicyAdapter:
    def __init__(self, initial_policy):
        self.policy = initial_policy
        self.adaptation_rate = 0.1
        self.successful_sequences = []
        self.token_frequencies = Counter()

    def adapt_policy(self, successful_sequence, score):
        self.successful_sequences.append((successful_sequence, score))
        
        # Update token frequencies
        self.update_token_frequencies(successful_sequence)
        
        # Adapt generation parameters
        self.adapt_generation_params(score)
        
        # Adapt base prompt
        self.adapt_base_prompt()
        
        # Adapt forbidden words
        self.adapt_forbidden_words()
        
        return self.policy

    def update_token_frequencies(self, sequence):
        for token in sequence:
            self.token_frequencies[token] += 1

    def adjust_param(self, current_value, score, lower_bound, upper_bound):
            if score > self.policy.get('average_score', 0):
                new_value = current_value * (1 - self.adaptation_rate)
            else:
                new_value = current_value * (1 + self.adaptation_rate)
            return max(lower_bound, min(new_value, upper_bound))

    def adapt_generation_params(self, score):
        params = self.policy['generation_params']

        # Adjust temperature
        params['temperature'] = self.adjust_param(
            params['temperature'], score, lower_bound=0.1, upper_bound=2.0
        )

        # Adjust top_p
        params['top_p'] = self.adjust_param(
            params['top_p'], score, lower_bound=0.1, upper_bound=1.0
        )

    def adapt_base_prompt(self):
        most_common = self.token_frequencies.most_common(5)
        frequent_tokens = [token for token, _ in most_common]
        
        additional_context = f" Consider using these relevant terms: {', '.join(frequent_tokens)}."
        if len(self.policy['base_prompt'] + additional_context) <= 500:  # Limit prompt length
            self.policy['base_prompt'] += additional_context

    def adapt_forbidden_words(self):
        low_scoring_words = set()
        threshold = np.median([score for _, score in self.successful_sequences])
        for sequence, score in self.successful_sequences[-10:]:  # Consider last 10 sequences
            if score < threshold:
                low_scoring_words.update(sequence)
        
        current_forbidden = set(self.policy.get('forbidden_words', []))
        new_forbidden = list(current_forbidden.union(low_scoring_words))[:20]  # Limit to 20 forbidden words
        self.policy['forbidden_words'] = new_forbidden

class Scorer:
    def __init__(self, config):

        # Initialize sentiment analysis model
        self.sentiment_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
        self.sentiment_tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
        self.sentiment_weight = config['sentiment_weight']
        self.grammar_weight = config['grammar_weight']
        self.bleu_weight = config['bleu_weight']
        self.keyword_weight = config['keyword_weight']
        self.length_weight = config['length_weight']

        # Initialize language tool for grammar checking
        self.language_tool = language_tool_python.LanguageTool('en-US')
        
        # Load your target/reference sentences for BLEU score
        self.reference_sentences = [
            "The quick brown fox jumps over the lazy dog.",
            "A journey of a thousand miles begins with a single step.",
            # Add more reference sentences relevant to your task
        ]
        
        
        # Keywords that should be rewarded if present
        self.keywords = ["innovative", "efficient", "sustainable", "user-friendly"]

    def score(self, sequence):
        text = ' '.join(sequence)
        
        # Compute individual scores
        sentiment_score = self.get_sentiment_score(text)
        grammar_score = self.get_grammar_score(text)
        bleu_score = self.get_bleu_score(text)
        keyword_score = self.get_keyword_score(text)
        length_score = self.get_length_score(text)
        
        # Combine scores (you can adjust weights based on importance)
        final_score = (
            self.sentiment_weight * sentiment_score +
            self.grammar_weight * grammar_score +
            self.bleu_weight * bleu_score +
            self.keyword_weight * keyword_score +
            self.length_weight * length_score
        )
        
        return final_score

    def get_sentiment_score(self, text):
        inputs = self.sentiment_tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
        with torch.no_grad():
            outputs = self.sentiment_model(**inputs)
        scores = torch.nn.functional.softmax(outputs.logits, dim=1)
        return scores[:, 1].item()  # Positive sentiment score

    def get_grammar_score(self, text):
        matches = self.language_tool.check(text)
        error_count = len(matches)
        return max(0, 1 - (error_count / len(text.split())))  # Normalize by word count

    def get_bleu_score(self, text):
        candidate = word_tokenize(text.lower())
        references = [word_tokenize(ref.lower()) for ref in self.reference_sentences]
        return sentence_bleu(references, candidate)

    def get_keyword_score(self, text):
        word_count = len(text.split())
        keyword_count = sum(1 for keyword in self.keywords if keyword.lower() in text.lower())
        return min(1, keyword_count / (word_count * 0.1))  # Expect about 10% of words to be keywords

    def get_length_score(self, text):
        ideal_length = 100  # Adjust based on your requirements
        actual_length = len(text.split())
        return max(0, 1 - abs(actual_length - ideal_length) / ideal_length)

def initialize_policy():
    policy = {
        'model': load_finetuned_llm('path_to_your_model'),
        'base_prompt': "You are an AI assistant tasked with [specific task]. ",
        'generation_params': {
            'temperature': 0.7,
            'top_p': 0.9,
            'max_tokens': 100
        },
        'action_history': [],
        'performance_metrics': {
            'cumulative_reward': 0,
            'successful_sequences': []
        }
    }
    return policy

def load_config(config_path):
    with open(config_path, 'r') as file:
        return yaml.safe_load(file)

def main(args):
    config = load_config(args.config)
    
    beam_nrpa = BeamNRPA(config)
    best_sequence, best_score = beam_nrpa.run()
    
    print(f"Best sequence: {' '.join(best_sequence)}")
    print(f"Best score: {best_score}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Run Beam NRPA with a fine-tuned language model.")
    parser.add_argument('--config', type=str, default='config.yaml', help='Path to the YAML configuration file')
    args = parser.parse_args()
    
    main(args)