src/deepeval/base_task.py

from abc import ABC, abstractmethod
from datasets import load_dataset
import os
from dotenv import load_dotenv
import openai
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessorList, LogitsProcessor
import torch
from typing import List
from datetime import datetime
load_dotenv()
HF_TOKEN=os.getenv("HF_TOKEN")
OPENAI_KEY = os.getenv("OPENAI_API_KEY")

class BaseTask(ABC):
    _model_cache = {}  # Class-level cache for models and tokenizers

    def __init__(self, dataset_repo, model_name):
        self.dataset_repo = dataset_repo
        self.dataset = self.load_dataset_from_hf()

        device_count = torch.cuda.device_count()
        if device_count > 1:
            self.device = "auto"
            print(f"Using {device_count} GPUs with auto config.")
        elif device_count == 1:
            self.device = "cuda"
            print(f"Using {device_count} GPU with cuda config.")
        else:
            self.device = "cpu"
            print("No GPU found. Using CPU.")

        self.model, self.tokenizer = self.get_cached_model(model_name, self.device)
        openai.api_key = OPENAI_KEY


    @classmethod
    def get_cached_model(cls, model_name, device):
        """Ensures the same model and tokenizer are used for every instance of subclasses."""
        if model_name not in cls._model_cache:
            cls._model_cache[model_name] = cls.load_model(model_name, device)
        return cls._model_cache[model_name]
    
    @staticmethod
    def load_model(model_name: str, device):
        """Loads model and tokenizer once and caches it."""
        print(f"Loading model: {model_name}")
        start_time = datetime.now()
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map=device,
            token=HF_TOKEN,  # Replace with actual token
        )
        end_time = datetime.now()
        print(f"Model loaded in {(end_time - start_time).seconds} seconds.")
        print("Model loaded.")
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        return model, tokenizer

    # @staticmethod
    # def load_model(model_name: str, device, weight, dtype, base_model):
    #     """Loads model and tokenizer once and caches it."""
    #     print(f"Loading model: {model_name}")
    #     start_time = datetime.now()
    #     if weight == "Adapter":
    #         base_model_1 = AutoModelForCausalLM.from_pretrained(
    #             base_model,
    #             torch_dtype=dtype,
    #             device_map=device,
    #             token=HF_TOKEN,  # Replace with actual token
    #         )
    #         model = PeftModel.from_pretrained(base_model_1, base_model)
    #         tokenizer = AutoTokenizer.from_pretrained(base_model)
    #         end_time = datetime.now()
    #     else:
    #         model = AutoModelForCausalLM.from_pretrained(
    #             model_name,
    #             torch_dtype=dtype,
    #             device_map=device,
    #             token=HF_TOKEN,  # Replace with actual token
    #         )
    #         tokenizer = AutoTokenizer.from_pretrained(model_name)
    #         end_time = datetime.now()
    #     print(f"Model loaded in {(end_time - start_time).seconds} seconds.")
    #     print("Model loaded.")
        
    #     return model, tokenizer


    def generate_response_mcqa(self, msg, max_new_tokens=1, choices: List[str]=[]):
        # Ensure the tokenizer has a padding token
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token  # Use EOS token as PAD token

        inputs = self.tokenizer(msg, return_tensors="pt", padding=True, truncation=True)
        input_ids = inputs.input_ids
        attention_mask = inputs.attention_mask

        if self.model.config.pad_token_id is None:
            self.model.config.pad_token_id = self.tokenizer.eos_token_id

        # Get token IDs for answer choices
        valid_answers = choices
        valid_token_ids = [self.tokenizer.convert_tokens_to_ids(ans) for ans in valid_answers]

        class MultipleChoiceLogitsProcessor:
            def __call__(self, input_ids, scores):
                mask = torch.full_like(scores, float("-inf"))
                mask[:, valid_token_ids] = scores[:, valid_token_ids]  # Allow only valid tokens
                return mask

        logits_processor = LogitsProcessorList([MultipleChoiceLogitsProcessor()])

        output = self.model.generate(
            input_ids,
            attention_mask=attention_mask,  # Fix: Pass attention_mask to avoid warning
            max_new_tokens=max_new_tokens,
            logits_processor=logits_processor
        )
        answer = self.tokenizer.decode(output[0][-1])

        return answer
    
    def generate_response_mcqa_multi_token(self, msg, max_new_tokens=2, choices: list = []):
        """
        Handles multiple-choice questions where answers might have multiple tokens.
        """
        # Ensure tokenizer has proper special tokens set
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token

        if self.model.config.pad_token_id is None:
            self.model.config.pad_token_id = self.tokenizer.pad_token_id

        chat = [
                {"role": "user", "content": "You are a multiple choice question-answering chatbot. Do not give an answer that is not included in the choices. Only answer with letters like A, B, C, D..."},
                {"role": "assistant", "content": "I am ready to answer your questions. Feel free to ask anything.\n"},
                {"role": "user", "content": f"{msg}"},
            ]
        formatted_chat = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
        #print(formatted_chat)
        inputs = self.tokenizer(formatted_chat, return_tensors="pt", padding=True, truncation=True)

        if self.device == "auto":
            input_ids = inputs.input_ids
            attention_mask = inputs.attention_mask
        else:
            input_ids = inputs.input_ids.to(self.model.device)
            attention_mask = inputs.attention_mask.to(self.model.device)

        # Generate the sequence of letters starting from 'A'
        letters = [chr(ord('A') + i) for i in range(len(choices))]  # Create option letters A, B, C, D, E, ...
        encoded_choices = [self.tokenizer.encode(letter, add_special_tokens=False) for letter in letters]
        flattened_encoded_choices = [item for sublist in encoded_choices for item in sublist]  # Flatten the list
        #print(flattened_encoded_choices)

        allowed_tokens = flattened_encoded_choices
        allowed_tokens += self.get_chat_template_tokens() # Get the special chat tokens
        allowed_token_ids = set(allowed_tokens)  # Ensure uniqueness

        # Custom LogitsProcessor to restrict generation
        class RestrictToABCDLogitsProcessor(LogitsProcessor):
            def __call__(self, input_ids, scores):
                mask = torch.full_like(scores, float("-inf"))  # Block all tokens
                mask[:, list(allowed_token_ids)] = scores[:, list(allowed_token_ids)]  # Allow only A, B, C, D tokens
                return mask
        logits_processor = LogitsProcessorList([RestrictToABCDLogitsProcessor()])

        # Generate response
        output = self.model.generate(
            input_ids,
            do_sample=True,
            attention_mask=attention_mask,
            max_new_tokens=max_new_tokens,
            eos_token_id=self.tokenizer.eos_token_id,
            pad_token_id=self.tokenizer.pad_token_id,
            temperature=0.4,
            logits_processor=logits_processor,
        )
        generated_ids = output[0]  # The generated sequence including the prompt
        generated_tokens = generated_ids[len(input_ids[0]):]  # Exclude the input_ids part
        generated_text = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
        return generated_text

    def generate_response(self, prompt: str, max_new_tokens: int = 100) -> str:

        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token

        if self.model.config.pad_token_id is None:
            self.model.config.pad_token_id = self.tokenizer.eos_token_id

        chat = [
            {"role": "user", "content": "You are a helpful AI assistant."},
            {"role": "assistant", "content": "I am here to help you with any questions you may have."},
            {"role": "user", "content": prompt},
        ]
        
        formatted_chat = self.tokenizer.apply_chat_template(
            chat,
            tokenize=False,
            add_generation_prompt=True
        )

        inputs = self.tokenizer(formatted_chat, return_tensors="pt", padding=True, truncation=True)

        if self.device == "auto":
            input_ids = inputs.input_ids
            attention_mask = inputs.attention_mask
        else:
            input_ids = inputs.input_ids.to(self.model.device)
            attention_mask = inputs.attention_mask.to(self.model.device)

        output = self.model.generate(
            input_ids,
            attention_mask=attention_mask,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.7,
        )

        generated_ids = output[0]
        prompt_len = input_ids.shape[1]
        generated_tokens = generated_ids[prompt_len:]
        result = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
        return result

    def get_chat_template_tokens(self):
        allowed_token_chat = [
            {"role": "user", "content": ""},
            {"role": "assistant", "content": ""}
        ]
        allowed_special_tokens = self.tokenizer.apply_chat_template(allowed_token_chat, tokenize=True)
        return allowed_special_tokens

    @abstractmethod
    def load_dataset_from_hf(self):
        """
        Define your own loading method if needed.
        :return: Dataset
        """
        print("Loading dataset from Hugging Face.")
        start_time = datetime.now()
        dataset= load_dataset(self.dataset_repo, token=HF_TOKEN, split="train")
        print("Dataset loaded.")
    
        # Load 50 from each dataset
        if len(dataset) > 50:
            dataset = dataset.shuffle(seed=42).select(range(50))
        end_time = datetime.now()
        print(f"Dataset loaded in {(end_time - start_time).seconds} seconds.")
        return dataset
    
    def load_dataset_lmjudge_from_hf(self):
        """
        Define your own loading method if needed.
        :return: Dataset
        """
        print("Loading dataset from Hugging Face.")
        start_time = datetime.now()
        dataset= load_dataset(self.dataset_repo, token=HF_TOKEN, split="train")
        print("Dataset loaded.")
        
        # Load 50 from each dataset
        if len(dataset) > 10:
            dataset = dataset.shuffle(seed=42).select(range(10))
        end_time = datetime.now()
        print(f"Dataset loaded in {(end_time - start_time).seconds} seconds.")
        return dataset

    @abstractmethod
    def evaluate(self):
        pass