Feature LLM based Curation

dqc/__init__.py

@@ -1,4 +1,5 @@
 from .crossval import CrossValCurate
+from .llm import LLMCurate
 from .version import __version__, show_versions
 
-__all__ = ["CrossValCurate"]
+__all__ = ["CrossValCurate", "LLMCurate"]

dqc/base.py

@@ -1,6 +1,9 @@
+import random
 from abc import ABC, abstractmethod
 from typing import Union
 
+import numpy as np
+import torch
 from pandas._typing import RandomState
 
 
@@ -15,10 +18,19 @@ class BaseCurate(ABC):
 
     def __init__(
         self,
-        random_state: RandomState = 42,
+        random_state: Union[int, RandomState] = 42,
         **options,
     ):
         self.random_state = random_state
+        self._set_seed(random_state)
+
+    def _set_seed(self, seed):
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
 
     @abstractmethod
     def fit_transform(self): ...

dqc/llm.py

@@ -0,0 +1,178 @@
+from typing import Callable, List, Tuple, Union
+
+import pandas as pd
+from datasets import Dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from dqc.base import BaseCurate
+from dqc.llm_utils import (
+    _empty_ds_ensemble_handler,
+    _validate_init_params,
+    _validate_run_params,
+    compute_selfensembling_confidence_score,
+    run_LLM,
+)
+from dqc.utils import Logger
+
+logger = Logger("DQC-Toolkit")
+
+
+class LLMCurate(BaseCurate):
+    """
+    Args:
+        model (AutoModelForCausalLM): Instantiated LLM
+        tokenizer (AutoTokenizer): Instantiated tokenizer corresponding to the `model`
+        verbose (bool, optional): Sets the verbosity level during execution. `True` indicates logging level INFO and `False` indicates logging level 'WARNING'. Defaults to False.
+
+    Examples:
+
+    """
+
+    def __init__(
+        self,
+        model: AutoModelForCausalLM,
+        tokenizer: AutoTokenizer,
+        verbose: bool = False,
+        **options,
+    ):
+        super().__init__(**options)
+
+        _validate_init_params(model, tokenizer, verbose)
+        self.model = model
+        self.tokenizer = tokenizer
+        self.verbose = verbose
+        self._set_verbosity(verbose)
+
+        self.ds_ensemble = None
+
+    def __str__(self):
+        display_dict = self.__dict__.copy()
+
+        for key in list(display_dict.keys()):
+            if key in ["ds_ensemble"]:
+                ## Don't need to display these attributes
+                del display_dict[key]
+
+        return str(display_dict)
+
+    __repr__ = __str__
+
+    def _set_verbosity(self, verbose: bool):
+        """Set logger level based on user input for parameter `verbose`
+
+        Args:
+            verbose (bool): Indicator for verbosity
+        """
+        if verbose:
+            logger.set_level("INFO")
+        else:
+            logger.set_level("WARNING")
+
+    def fit_transform(self):
+        pass
+
+    def run(
+        self,
+        column_to_curate: str,
+        data: Union[pd.DataFrame, Dataset] = None,
+        ds_column_mapping: dict = {},
+        prompt_variants: List[str] = [""],
+        skip_llm_inference: bool = False,
+        llm_response_cleaned_column_list: List[str] = ["reference_prediction"],
+        return_scores: bool = True,
+        answer_start_token: str = "",
+        answer_end_token: str = "",
+        scoring_params: dict = {
+            "scoring_method": "exact_match",
+            "case_sensitive": False,
+        },
+        **options,
+    ) -> Dataset:
+        """Run LLMCurate on the input data
+
+        Args:
+            column_to_curate (str): Column name in `data` with the text that needs to be curated
+            data (Union[pd.DataFrame, Dataset]): Input data for LLM based curation
+            ds_column_mapping (dict, optional): Mapping of entities to be used in the LLM prompt to the corresponding columns in the input data. Defaults to {}.
+            prompt_variants (List[str], optional): List of different LLM prompts to be used to curate the labels under `column_to_curate`. Defaults to [''].
+            skip_llm_inference (bool, optional): Indicator variable to prevent re-running LLM inference. Set to `True` if artifacts from the previous run of LLMCurate needs to be reused. Else `False`. Defaults to False.
+            llm_response_cleaned_column_list (list, optional): Names of the columns that will contain LLM predictions for each input prompt in `prompt_variants`. Defaults to ['reference_prediction'].
+            return_scores (bool, optional): Indicator variable set to `True` if label confidence scores are to be computed for each label under `column_to_curate`. Defaults to True.
+            answer_start_token (str, optional): Token that indicates the start of answer generation. Defaults to ''
+            answer_end_token (str, optional): Token that indicates the end of answer generation. Defaults to ''
+            scoring_params (dict, optional): Parameters related to util function `compute_selfensembling_confidence_score` to compute confidence scores of `column_to_curate`
+
+        Returns:
+            Dataset: Input dataset with reference responses. If `return_scores=True`, then input dataset with reference responses and confidence scores.
+        """
+        if not skip_llm_inference:
+            empty_string_col_list = _validate_run_params(
+                data,
+                column_to_curate,
+                ds_column_mapping,
+                prompt_variants,
+                llm_response_cleaned_column_list,
+            )
+
+            if len(empty_string_col_list) > 0:
+                logger.warning(
+                    "Found empty string(s) in the input data under column(s) {empty_string_col_list}"
+                )
+
+            logger.info(
+                f"Running the LLM to generate the {len(prompt_variants)} reference responses using `prompt_variants`.."
+            )
+            ds_ensemble = None
+
+            model = self.model
+            tokenizer = self.tokenizer
+
+            for index, prompt_template_prefix in enumerate(prompt_variants):
+                proposed_answer_col_name = llm_response_cleaned_column_list[index]
+                ds = run_LLM(
+                    data,
+                    model,
+                    tokenizer,
+                    ds_column_mapping=ds_column_mapping,
+                    prompt_template_prefix=prompt_template_prefix,
+                    answer_start_token=answer_start_token,
+                    answer_end_token=answer_end_token,
+                    llm_response_cleaned_col_name=proposed_answer_col_name,
+                    random_state=self.random_state,
+                    **options,
+                )
+
+                if not ds_ensemble:
+                    ds_ensemble = ds
+                else:
+                    ds_ensemble = ds_ensemble.add_column(
+                        proposed_answer_col_name, ds[proposed_answer_col_name]
+                    )
+            self.ds_ensemble = ds_ensemble
+
+        if return_scores:
+            if skip_llm_inference:
+                if (
+                    isinstance(data, pd.DataFrame)
+                    or ds_column_mapping
+                    or prompt_variants
+                ):
+                    logger.warning(
+                        "Ignoring params `data`, `ds_column_mapping` and `prompt_variants` since `skip_llm_inference` is set to `True`"
+                    )
+
+            _empty_ds_ensemble_handler(len(self.ds_ensemble) == 0, skip_llm_inference)
+
+            logger.info(
+                "Computing confidence scores using the LLM reference responses.."
+            )
+            self.ds_ensemble = self.ds_ensemble.map(
+                compute_selfensembling_confidence_score,
+                fn_kwargs={
+                    "target_column": column_to_curate,
+                    "reference_column_list": llm_response_cleaned_column_list,
+                    **scoring_params,
+                },
+            )
+
+        return self.ds_ensemble

dqc/llm_utils/__init__.py

@@ -0,0 +1,14 @@
+from ._sanity_checks import (
+    _empty_ds_ensemble_handler,
+    _validate_init_params,
+    _validate_run_params,
+)
+from .compute_confidence_score import compute_selfensembling_confidence_score
+from .inference import build_LLM_prompt, infer_LLM, run_LLM
+
+__all__ = [
+    "build_LLM_prompt",
+    "compute_selfensembling_confidence_score",
+    "infer_LLM",
+    "run_LLM",
+]