diff --git a/.gitignore b/.gitignore index 150885e..2539a18 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ __pycache__/ *.py[codz] *$py.class +*log # C extensions *.so @@ -215,4 +216,4 @@ __marimo__/ # Streamlit .streamlit/secrets.toml -**/*.jsonl \ No newline at end of file +**/*.jsonl diff --git a/README.md b/README.md index 3bdf624..b557b07 100644 --- a/README.md +++ b/README.md @@ -84,6 +84,12 @@ The annotation process includes two filtering stages. Heuristic-based filters en uv run psilo dataset filter ``` +### Uncertainty +Use the following command to run uncertainty quantification methods: +```bash +uv run psilo methods uncertainty +``` + ## Citation ``` @misc{rykov2025modelslielearnmultilingual, diff --git a/psilo/cli.py b/psilo/cli.py index 57084e7..34c3fdb 100644 --- a/psilo/cli.py +++ b/psilo/cli.py @@ -1,9 +1,10 @@ import typer from dataset.cli import app as dataset_app +from methods.cli import app as methods_app app = typer.Typer(no_args_is_help=True, add_completion=False) app.add_typer(dataset_app, name="dataset") - +app.add_typer(methods_app, name="methods") def main(): app() diff --git a/psilo/methods/__init__.py b/psilo/methods/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/psilo/methods/cli.py b/psilo/methods/cli.py new file mode 100644 index 0000000..7841dff --- /dev/null +++ b/psilo/methods/cli.py @@ -0,0 +1,39 @@ +import typer +from loguru import logger +from .uncertainty.recompute_logits_with_uq import run_uncertainty_evaluation +from .uncertainty.evaluation import evaluate_uncertainty +from datasets import load_dataset + +app = typer.Typer(help="PsiloQA Methods Pipeline") + +@app.command("uncertainty") +def uncertainty(): + logger.info("uncertainty evaluation cli") + + dataset_val = load_dataset("s-nlp/PsiloQA", split="validation").to_pandas() + dataset_test = load_dataset("s-nlp/PsiloQA", split="test").to_pandas() + + logger.info("loaded datasets") + + logger.info("starting validation evaluation") + result_val = run_uncertainty_evaluation(dataset=dataset_val) + logger.info("finished validation evaluation") + + logger.info("starting test evaluation") + result_test = run_uncertainty_evaluation(dataset=dataset_test) + logger.info("finished test evaluation") + + logger.info("starting results aggregation") + evaluate_uncertainty(result_val, result_test) + logger.info("finished evaluation") + +@app.command("encoder_train") +def encoder_train(): + logger.info("encoder train cli") + +@app.command("encoder_eval") +def encoder_eval(): + logger.info("encoder evaluation cli") + +if __name__ == "__main__": + app() \ No newline at end of file diff --git a/psilo/methods/encoder/__init__.py b/psilo/methods/encoder/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/psilo/methods/uncertainty/README.md b/psilo/methods/uncertainty/README.md new file mode 100644 index 0000000..9c74b26 --- /dev/null +++ b/psilo/methods/uncertainty/README.md @@ -0,0 +1,6 @@ +# Token-Level Uncertainty Quantification with LM-Polygraph + +This code is adapted from the [LM-Polygraph](https://github.com/IINemo/lm-polygraph) project, +developed by Fadeeva et al. (2023) and released under the MIT License. +The original project provides a comprehensive framework for uncertainty quantification of large language models. +Modifications have been made to suit token-level uncertainty quantification cases in this script. \ No newline at end of file diff --git a/psilo/methods/uncertainty/__init__.py b/psilo/methods/uncertainty/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/psilo/methods/uncertainty/estimators/claim_conditioned_probability.py b/psilo/methods/uncertainty/estimators/claim_conditioned_probability.py new file mode 100644 index 0000000..b5c8acf --- /dev/null +++ b/psilo/methods/uncertainty/estimators/claim_conditioned_probability.py @@ -0,0 +1,65 @@ +import numpy as np + +from typing import Dict + + +class ClaimConditionedProbability(): + def __init__(self): + super().__init__() + + def __str__(self): + return "CCP" + + def _reduce(self, logprobs: list[float]): + return logprobs + + def _combine_nli(self, forward: str, backward: str): + """ + Combines two NLI predictions NLI(x, y) and NLI(y, x) into a single prediction. + + Prioritizes "entail" or "contra" if present, otherwise returns "neutral". + """ + if forward == backward: + return forward + if all(x in [forward, backward] for x in ["entail", "contra"]): + return "neutral" + for x in ["entail", "contra"]: + if x in [forward, backward]: + return x + return "neutral" + + def __call__(self, stats: Dict[str, np.ndarray]) -> np.ndarray: + words = stats["greedy_tokens"] + alternatives = stats["greedy_tokens_alternatives"] + alternatives_nli = stats["greedy_tokens_alternatives_nli"] + prob_nli = [] + for sample_words, sample_alternatives, sample_alternatives_nli in zip( + words, + alternatives, + alternatives_nli, + ): + sample_mnlis = [] + for word, word_alternatives, word_alternatives_nli in zip( + sample_words, + sample_alternatives, + sample_alternatives_nli, + ): + entail_logprobs, entail_words = [], [] + contra_logprobs, contra_words = [], [] + for i in range(len(word_alternatives)): + word_alt, logprob = word_alternatives[i] + nli_outcome = self._combine_nli( + word_alternatives_nli[0][i], + word_alternatives_nli[i][0], + ) + if i == 0 or nli_outcome == "entail": + entail_logprobs.append(logprob) + entail_words.append(word_alt) + elif nli_outcome == "contra": + contra_logprobs.append(logprob) + contra_words.append(word_alt) + entail_logprob = np.logaddexp.reduce(entail_logprobs) + total_logprob = np.logaddexp.reduce(entail_logprobs + contra_logprobs) + sample_mnlis.append(entail_logprob - total_logprob) + prob_nli.append(self._reduce(sample_mnlis)) + return -np.array(prob_nli) diff --git a/psilo/methods/uncertainty/estimators/focus.py b/psilo/methods/uncertainty/estimators/focus.py new file mode 100644 index 0000000..ae45781 --- /dev/null +++ b/psilo/methods/uncertainty/estimators/focus.py @@ -0,0 +1,342 @@ +import os +import numpy as np +import torch +import math + +from typing import Dict + +import pickle +from tqdm import tqdm +from torch.nn import NLLLoss + +from datasets import load_dataset +from collections import defaultdict +import random +import logging + +from transformers import AutoTokenizer +import spacy + + +log = logging.getLogger(__name__) + + +def calcu_idf( + tokenizer_path, path, idf_dataset, trust_remote_code, idf_seed, idf_dataset_size +): + os.makedirs(os.path.dirname(path), exist_ok=True) + dataset = load_dataset(idf_dataset, trust_remote_code=trust_remote_code) + data = [d for d in dataset["train"]] + random.seed(idf_seed) + random.shuffle(data) + if (idf_dataset_size > 0) and (idf_dataset_size < len(data)): + data = random.sample(data, idf_dataset_size) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True) + document_frequency = defaultdict(int) + offset = 1 if "facebook" in tokenizer_path else 0 + for doc in tqdm(data): + tokenized_doc = tokenizer(doc["text"])["input_ids"][offset:] + unique_tokens = set(tokenized_doc) + for token in unique_tokens: + document_frequency[token] += 1 + total_documents = len(data) + pickle.dump( + np.array( + [ + math.log(total_documents / (document_frequency[i] + 1)) + for i in range(len(tokenizer.vocab)) + ] + ), + open(path, "wb"), + ) + + +class Focus(): + def __init__( + self, + gamma: float, + p: float, + model_name: str, + path: str, + idf_dataset: str, + trust_remote_code: bool, + idf_seed: int, + idf_dataset_size: int, + spacy_path: str, + ): + super().__init__() + if not spacy.util.is_package(spacy_path): + spacy.cli.download(spacy_path) + if not os.path.exists(path): + calcu_idf( + model_name, + path, + idf_dataset, + trust_remote_code, + idf_seed, + idf_dataset_size, + ) + self.token_idf = pickle.load(open(path, "rb")) + self.NER_type = [ + "PERSON", + "DATE", + "ORG", + "GPE", + "NORP", + "ORDINAL", + "PRODUCT", + "CARDINAL", + "LOC", + "FAC", + "EVENT", + "WORK_OF_ART", + "LAW", + "LANGUAGE", + "TIME", + "PERCENT", + "MONEY", + "QUANTITY", + ] + self.pos_tag = ["NOUN", "NUM", "PROPN"] + self.start_token_idx = 999999 + self.p = p + self.gamma = gamma + self.nlp = spacy.load(spacy_path) + + def __str__(self): + return f"Focus (gamma={self.gamma})" + + def entropy(self, p): + p_torch = torch.tensor(p) + return torch.sum( + -torch.where(p_torch > 0, p_torch * p_torch.log2(), p_torch.new([0.0])), + dim=-1, + ).numpy() + + def __call__(self, stats: Dict[str, np.ndarray]) -> np.ndarray: + attention_weights = [np.max(weights, axis=(0,1)) for weights in stats["attention_all"]] + greedy_log_probs = stats["greedy_log_probs"] + greedy_tokens = stats["greedy_tokens"] + greedy_texts = stats["greedy_texts"] + tokenizer = stats["tokenizer"] + loss_fct = NLLLoss(reduction="none") + + focus_ue = [] + for greedy_log_prob, attention_weight, greedy_token, greedy_text in zip( + greedy_log_probs, attention_weights, greedy_tokens, greedy_texts + ): + sentence = self.nlp(greedy_text) + decodings = tokenizer.batch_decode(greedy_token, skip_special_tokens=True) + span_index = 0 + kw_mask = np.zeros_like(greedy_token, dtype=bool) + try: + for token_index, token in enumerate(decodings): + while (token.strip() not in sentence[span_index].text) and ( + sentence[span_index].text not in token.strip() + ): + span_index += 1 + span = sentence[span_index] + if span.text not in self.NER_type and ( + span.ent_type_ in self.NER_type or span.pos_ in self.pos_tag + ): + kw_mask[token_index] = True + except Exception as e: + log.error(e, exc_info=True) + log.info(decodings, sentence) + pass + + prob = np.exp(greedy_log_prob) + mask = prob < self.p + # only focus on keywords like NER + prob[mask] = 0 + if prob.shape[-1] > len(self.token_idf): + prob[:, : len(self.token_idf)] = ( + prob[:, : len(self.token_idf)] * self.token_idf + ) + else: + prob = prob * self.token_idf + prob = prob / np.sum(prob, axis=-1, keepdims=True) + entropy = np.exp2(self.entropy(prob)) + + ll = loss_fct( + torch.log(torch.tensor(prob) + 1e-10), torch.tensor(greedy_token) + ) + hc = ll + entropy + + if not kw_mask.sum(): + focus_ue.append(hc.mean()) + continue + # w(i,j) estimation and penalty estimation for a new gallucination score + weight = attention_weight[kw_mask] / ( + np.sum(attention_weight[kw_mask], axis=1, keepdims=True) + 1e-6 + ) + weight = np.zeros_like(attention_weight) + weight[kw_mask] = attention_weight[kw_mask] / ( + np.sum(attention_weight[kw_mask], axis=1, keepdims=True) + 1e-6 + ) + token_focus = [] + + hc = hc.numpy() + mask = np.isnan(hc).astype(bool) + hc[mask] = hc[~mask].max() + for i, token_weights in enumerate(weight): + ue = hc[i] + if len(token_focus): + ue += ( + self.gamma + * ( + np.array(token_focus) * token_weights[: len(token_focus)] + ).sum() + ) + token_focus.append(ue) + focus_ue.append(np.array(token_focus) * kw_mask) + + return np.array(focus_ue) + + +class FocusClaim(): + def __init__( + self, + gamma: float, + p: float, + model_name: str, + path: str, + idf_dataset: str, + trust_remote_code: bool, + idf_seed: int, + idf_dataset_size: int, + spacy_path: str, + ): + super().__init__() + + if not spacy.util.is_package(spacy_path): + spacy.cli.download(spacy_path) + if not os.path.exists(path): + calcu_idf( + model_name, + path, + idf_dataset, + trust_remote_code, + idf_seed, + idf_dataset_size, + ) + self.token_idf = pickle.load(open(path, "rb")) + self.NER_type = [ + "PERSON", + "DATE", + "ORG", + "GPE", + "NORP", + "ORDINAL", + "PRODUCT", + "CARDINAL", + "LOC", + "FAC", + "EVENT", + "WORK_OF_ART", + "LAW", + "LANGUAGE", + "TIME", + "PERCENT", + "MONEY", + "QUANTITY", + ] + self.pos_tag = ["NOUN", "NUM", "PROPN"] + self.p = p + self.gamma = gamma + self.nlp = spacy.load(spacy_path) + + def __str__(self): + return f"FocusClaim (gamma={self.gamma})" + + def entropy(self, p): + p_torch = torch.tensor(p) + return torch.sum( + -torch.where(p_torch > 0, p_torch * p_torch.log2(), p_torch.new([0.0])), + dim=-1, + ).numpy() + + def __call__(self, stats: Dict[str, np.ndarray]) -> np.ndarray: + attention_weights = [np.max(weights, axis=0) for weights in stats["attention_all"]] + greedy_log_probs = stats["greedy_log_probs"] + greedy_tokens = stats["greedy_tokens"] + greedy_texts = stats["greedy_texts"] + tokenizer = stats["tokenizer"] + claims = stats["claims"] + loss_fct = NLLLoss(reduction="none") + + focus_ue = [] + for ( + greedy_log_prob, + attention_weight, + greedy_token, + greedy_text, + claims_i, + ) in zip( + greedy_log_probs, attention_weights, greedy_tokens, greedy_texts, claims + ): + sentence = self.nlp(greedy_text) + decodings = tokenizer.batch_decode(greedy_token, skip_special_tokens=True) + span_index = 0 + kw_mask = np.zeros_like(greedy_token, dtype=bool) + try: + for token_index, token in enumerate(decodings): + while (token.strip() not in sentence[span_index].text) and ( + sentence[span_index].text not in token.strip() + ): + span_index += 1 + span = sentence[span_index] + if span.text not in self.NER_type and ( + span.ent_type_ in self.NER_type or span.pos_ in self.pos_tag + ): + kw_mask[token_index] = True + except Exception as e: + log.error(e, exc_info=True) + log.info(decodings, sentence) + pass + + prob = np.exp(greedy_log_prob) + mask = prob < self.p + prob[mask] = 0 + if prob.shape[-1] > len(self.token_idf): + prob[:, : len(self.token_idf)] = ( + prob[:, : len(self.token_idf)] * self.token_idf + ) + else: + prob = prob * self.token_idf + prob = prob / np.sum(prob, axis=-1, keepdims=True) + entropy = np.exp2(self.entropy(prob)) + ll = loss_fct( + torch.log(torch.tensor(prob) + 1e-10), torch.tensor(greedy_token) + ) + hc = ll + entropy + + if not kw_mask.sum(): + kw_mask = np.ones_like(greedy_token, dtype=bool) + + kw_mask = kw_mask.flatten() + + weight = np.zeros_like(attention_weight) + weight[kw_mask] = attention_weight[kw_mask] / ( + np.sum(attention_weight[kw_mask], axis=1, keepdims=True) + 1e-6 + ) + + token_focus = [] + for i, token_weights in enumerate(weight): + ue = hc[i] + if len(token_focus): + ue += ( + self.gamma + * ( + np.array(token_focus) * token_weights[: len(token_focus)] + ).sum() + ) + token_focus.append(ue) + token_focus = np.array(token_focus) + + focus_ue.append([]) + for claim in claims_i: + tokens = np.array(claim.aligned_token_ids) + claim_p_i = token_focus[tokens] + focus_ue[-1].append(claim_p_i.mean()) + return focus_ue diff --git a/psilo/methods/uncertainty/estimators/greedy_alternatives_nli.py b/psilo/methods/uncertainty/estimators/greedy_alternatives_nli.py new file mode 100644 index 0000000..d269184 --- /dev/null +++ b/psilo/methods/uncertainty/estimators/greedy_alternatives_nli.py @@ -0,0 +1,247 @@ +import numpy as np + +from typing import Dict, List, Tuple + +from collections import defaultdict +import torch.nn as nn +import string + +import torch +from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, +) + + +class Deberta: + """ + Allows for the implementation of a singleton DeBERTa model which can be shared across + different uncertainty estimation methods in the code. + """ + + def __init__( + self, + deberta_path: str = "microsoft/deberta-large-mnli", + batch_size: int = 10, + device: str = None, + hf_cache: str = None, + ): + """ + Parameters + ---------- + deberta_path : str + huggingface path of the pretrained DeBERTa (default 'microsoft/deberta-large-mnli') + device : str + device on which the computations will take place (default 'cuda:0' if available, else 'cpu'). + """ + self.deberta_path = deberta_path + self.batch_size = batch_size + self._deberta = None + self._deberta_tokenizer = None + if device is None: + self.device = "cuda:0" if torch.cuda.is_available() else "cpu" + else: + self.device = device + self.hf_cache = hf_cache + self.setup() + + @property + def deberta(self): + if self._deberta is None: + self.setup() + + return self._deberta + + @property + def deberta_tokenizer(self): + if self._deberta_tokenizer is None: + self.setup() + + return self._deberta_tokenizer + + def to(self, device): + self.device = device + if self._deberta is not None: + self._deberta.to(self.device) + + def setup(self): + """ + Loads and prepares the DeBERTa model from the specified path. + """ + if self._deberta is not None: + return + self._deberta = AutoModelForSequenceClassification.from_pretrained( + self.deberta_path, + problem_type="multi_label_classification", + cache_dir=self.hf_cache, + ) + self._deberta_tokenizer = AutoTokenizer.from_pretrained( + self.deberta_path, cache_dir=self.hf_cache + ) + self._deberta.to(self.device) + self._deberta.eval() + + +def _eval_nli_model(nli_queue: List[Tuple[str, str]], deberta: Deberta) -> List[str]: + nli_set = list(set(nli_queue)) + + softmax = nn.Softmax(dim=1) + w_probs = defaultdict(lambda: defaultdict(lambda: None)) + for k in range(0, len(nli_set), deberta.batch_size): + batch = nli_set[k : k + deberta.batch_size] + encoded = deberta.deberta_tokenizer.batch_encode_plus( + batch, padding=True, return_tensors="pt" + ).to(deberta.device) + logits = deberta.deberta(**encoded).logits + logits = logits.detach().to(deberta.device) + for (wi, wj), prob in zip(batch, softmax(logits).cpu().detach()): + w_probs[wi][wj] = prob + + classes = [] + for w1, w2 in nli_queue: + pr = w_probs[w1][w2] + id = pr.argmax() + ent_id = deberta.deberta.config.label2id["ENTAILMENT"] + contra_id = deberta.deberta.config.label2id["CONTRADICTION"] + if id == ent_id: + str_class = "entail" + elif id == contra_id: + str_class = "contra" + else: + str_class = "neutral" + classes.append(str_class) + return classes + + +class GreedyAlternativesNLICalculator(): + @staticmethod + def meta_info() -> Tuple[List[str], List[str]]: + """ + Returns the statistics and dependencies for the SamplingPromptCalculator. + """ + return ["greedy_tokens_alternatives_nli"], ["greedy_tokens_alternatives"] + + def __init__(self, nli_model): + super().__init__() + self.nli_model = nli_model + + def _strip(self, w: str): + return w.strip(string.punctuation + " \n") + + def __call__( + self, + dependencies: Dict[str, np.array], + texts: List[str], + model, + max_new_tokens: int = 100, + **kwargs, + ) -> Dict[str, np.ndarray]: + greedy_alternatives = dependencies["greedy_tokens_alternatives"] + greedy_alternatives_nli = [] + for sample_alternatives in greedy_alternatives: + nli_matrixes = [] + for w_number, word_alternatives in enumerate(sample_alternatives): + nli_queue = [] + nli_matrix = [ + ["" for _ in range(len(word_alternatives))] + for _ in range(len(word_alternatives)) + ] + if len(word_alternatives) > 0 and not isinstance( + word_alternatives[0][0], + str, + ): + word_alternatives = [ + (dependencies["tokenizer"].decode([alt]), prob) + for alt, prob in word_alternatives + ] + words = [self._strip(alt[0]) for alt in word_alternatives] + for wi in words: + nli_queue.append((words[0], wi)) + nli_queue.append((wi, words[0])) + + nli_classes = _eval_nli_model(nli_queue, self.nli_model) + nli_class = defaultdict(lambda: None) + for nli_cl, (w1, w2) in zip(nli_classes, nli_queue): + nli_class[w1, w2] = nli_cl + + for i, wi in enumerate(words): + for j, wj in enumerate(words): + # Only calculate NLI with greedy token + if i > 0 and j > 0: + continue + nli_matrix[i][j] = nli_class[wi, wj] + + nli_matrixes.append(nli_matrix) + greedy_alternatives_nli.append(nli_matrixes) + + return {"greedy_tokens_alternatives_nli": greedy_alternatives_nli} + + +class GreedyAlternativesFactPrefNLICalculator(): + @staticmethod + def meta_info() -> Tuple[List[str], List[str]]: + """ + Returns the statistics and dependencies for the SamplingPromptCalculator. + """ + + return ["greedy_tokens_alternatives_fact_pref_nli"], [ + "greedy_tokens_alternatives", + "greedy_tokens", + "claims", + ] + + def __init__(self, nli_model): + super().__init__() + self.nli_model = nli_model + + def _strip(self, w: str): + return w.strip(string.punctuation + " \n") + + def __call__( + self, + dependencies: Dict[str, np.array], + texts: List[str], + model, + max_new_tokens: int = 100, + **kwargs, + ) -> Dict[str, np.ndarray]: + greedy_alternatives = dependencies["greedy_tokens_alternatives"] + greedy_tokens = dependencies["greedy_tokens"] + claims = dependencies["claims"] + greedy_alternatives_nli = [] + for sample_alternatives, sample_claims, sample_tokens in zip( + greedy_alternatives, + claims, + greedy_tokens, + ): + nli_queue = [] + for claim in sample_claims: + tokens = [sample_tokens[t] for t in claim.aligned_token_ids] + alts = [sample_alternatives[t] for t in claim.aligned_token_ids] + for i in range(len(tokens)): + for j in range(len(alts[i])): + text1 = model.tokenizer.decode(tokens[: i + 1]) + text2 = model.tokenizer.decode(tokens[:i] + [alts[i][j][0]]) + nli_queue.append((text1, text2)) + nli_queue.append((text2, text1)) + + nli_classes = _eval_nli_model(nli_queue, self.nli_model) + + nli_matrixes = [] + for claim in sample_claims: + nli_matrixes.append([]) + tokens = [sample_tokens[t] for t in claim.aligned_token_ids] + alts = [sample_alternatives[t] for t in claim.aligned_token_ids] + for i in range(len(tokens)): + nli_matrix = [] + for _ in range(len(alts[i])): + nli_matrix.append([]) + for j in range(len(alts[i])): + nli_matrix[-1].append(None) + for j in range(len(alts[i])): + nli_matrix[0][j], nli_matrix[j][0] = nli_classes[:2] + nli_classes = nli_classes[2:] + nli_matrixes[-1].append(nli_matrix) + greedy_alternatives_nli.append(nli_matrixes) + + return {"greedy_tokens_alternatives_fact_pref_nli": greedy_alternatives_nli} diff --git a/psilo/methods/uncertainty/estimators/max_probability.py b/psilo/methods/uncertainty/estimators/max_probability.py new file mode 100644 index 0000000..13acbd9 --- /dev/null +++ b/psilo/methods/uncertainty/estimators/max_probability.py @@ -0,0 +1,61 @@ +import numpy as np + +from typing import Dict + + +class SequenceProbability(): + """ + Estimates the sequence-level uncertainty of a language model by calculating the + log-probability of the generation with minus sign. + It is calculated as the sum of log-probabilities in each token. + Works only with whitebox models (initialized using lm_polygraph.utils.model.WhiteboxModel). + """ + + def __init__(self): + super().__init__() + + def __str__(self): + return "MaximumSequenceProbability" + + def __call__(self, stats: Dict[str, np.ndarray]) -> np.ndarray: + """ + Estimates the minus log-probability of each sample in input statistics. + + Parameters: + stats (Dict[str, np.ndarray]): input statistics, which for multiple samples includes: + * log p(y_i | y_ np.ndarray: + """ + Estimates the minus log-probability of each token in input statistics. + + Parameters: + stats (Dict[str, np.ndarray]): input statistics, which for multiple samples includes: + * log p(y_i | y_= thr).astype(int) + ids_pred = set(torch.where(torch.tensor(preds))[0].tolist()) + + if not ids_pred and not ids_labels: + iou = 1. + else: + iou = len(ids_labels & ids_pred) / len(ids_labels | ids_pred) + + thrs_row_uq.append(iou) + thr_val_lang[uq].append(thrs_row_uq) + + for uq in uq_methods: + step = (bounds[uq]["max"] - bounds[uq]["min"]) / 2000 + thrs = np.arange(bounds[uq]["min"], bounds[uq]["max"], step) + thr_val[lang][uq] = thrs[np.array(thr_val_lang[uq]).mean(0).argmax()] + + test_iou = {lang: {uq: [] for uq in uq_methods} for lang in df_test.lang.unique()} + test_ap = {lang: {uq: [] for uq in uq_methods} for lang in df_test.lang.unique()} + + for i, row in tqdm(df_test.iterrows(), total=df_test.shape[0]): + + span_labels = row_to_psiloqa_record(row)["labels"] + binary_labels = np.zeros(len(row.llm_answer)) + for span in span_labels: + binary_labels[span["start"] : span["end"]] = 1 + + for uq in uq_methods: + token_uq_scores = row.get(uq)[0] + if isinstance(token_uq_scores, float): + token_uq_scores = np.array(row.get("MSP")[0]) + + scores = tokens_to_char_mask(row.llm_answer, token_uq_scores, row.llm_checkpoint) + ids_labels = set(torch.where(torch.tensor(binary_labels))[0].tolist()) + + preds = (scores >= thr_val[row.lang][uq]).astype(int) + ids_pred = set(torch.where(torch.tensor(preds))[0].tolist()) + + if not ids_pred and not ids_labels: + iou = 1. + else: + iou = len(ids_labels & ids_pred) / len(ids_labels | ids_pred) + + test_iou[row.lang][uq].append(iou) + ap = average_precision_score(binary_labels, scores) + test_ap[row.lang][uq].append(ap) + + for lang in df_test.lang.unique(): + print(f"##### {lang} #####") + print("IOU:") + for uq in uq_methods: + print(f"{uq}: {(np.array(test_iou[lang][uq]).mean()*100):.2f}") + print() + print("AP:") + for uq in uq_methods: + print(f"{uq}: {(np.array(test_ap[lang][uq]).mean()*100):.2f}") diff --git a/psilo/methods/uncertainty/recompute_logits_with_uq.py b/psilo/methods/uncertainty/recompute_logits_with_uq.py new file mode 100755 index 0000000..712cfd7 --- /dev/null +++ b/psilo/methods/uncertainty/recompute_logits_with_uq.py @@ -0,0 +1,200 @@ +import argparse +from tqdm import tqdm +import pandas as pd +import numpy as np +import os + +from transformers import AutoModelForCausalLM, AutoTokenizer, BatchEncoding +from huggingface_hub import hf_hub_download +import torch + +from .utils.consts import * + +from .estimators.max_probability import * +from .estimators.focus import * +from .estimators.claim_conditioned_probability import * +from .estimators.greedy_alternatives_nli import GreedyAlternativesNLICalculator, Deberta + +TOP_K = 50 +device='cuda' if torch.cuda.is_available() else 'cpu' + +def calc_top_k_values(arr, k): + arr = np.array(arr) + # Get the indices that would sort the array in descending order + sorted_indices = np.argsort(arr)[::-1][:k] + + # Get the top k values and their corresponding indices + top_k_values = arr[sorted_indices].tolist() + top_k_indices = sorted_indices.tolist() + + return top_k_values, top_k_indices + +def get_logits(df): + # model_name = set(df.model_id).pop() + # Adapt to new column names: model_id -> llm_checkpoint, model_output_text -> llm_answer + model_name = set(df.llm_checkpoint).pop() + lang = set(df.lang).pop().lower() + + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) + model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", attn_implementation="eager") + + all_logits = {} + all_response_token_ids = {} + all_response_full_logits = {} + all_response_tokens_restored = {} + all_response_logits2token_id = {} + all_response_logits2tokens = {} + + all_msp = {} + all_ccp = {} + all_focus = {} + + nli_model = Deberta(device="cuda") + nli_model.setup() + calc_nli = GreedyAlternativesNLICalculator(nli_model=nli_model) + + for idx, ans in tqdm(df.iterrows(), desc=model_name, total=len(df)): + # Use the new column names in the chat template + prompt_len, inputs = LANG_MODEL2CHAT_TEMPLATE[lang][model_name].apply_chat_template(ans, tokenizer) + with torch.inference_mode(): + outputs = model(**inputs, output_attentions=True) + + outputs.scores = (outputs.logits[0, prompt_len-1:-1, :],) + outputs.sequences = inputs["input_ids"] + + methods = [SequenceProbability(), + ClaimConditionedProbability(), + Focus(gamma=0.9, p=0.01, model_name=model_name, path=f"../focus/{model_name}/token_idf.pkl", + idf_dataset="togethercomputer/RedPajama-Data-1T-Sample", idf_seed=42, spacy_path="en_core_web_sm", trust_remote_code=True, + idf_dataset_size=100000), + ] + + cut_logits = [] + cut_sequences = [] + cut_log_probs = [] + cut_alternatives = [] + lls = [] + attention_all = [] + + eos_token_id = -1 + model_inputs = inputs["input_ids"][:, :prompt_len] + n_alternatives = 10 + + all_logits_ = torch.stack(outputs.scores, dim=0) + for i in range(len(model_inputs)): + seq = outputs.sequences[i, model_inputs.shape[1]:].cpu() + + length = len(seq) + for j in range(len(seq)): + if seq[j] == eos_token_id: + length = j + 1 + break + + tokens = seq[:length].tolist() + cut_sequences.append(tokens) + + logits = all_logits_[i, :length, :].cpu() + cut_logits.append(logits.numpy()) + + log_probs = logits.log_softmax(-1) + cut_log_probs.append(log_probs.numpy()) + lls.append([log_probs[j, tokens[j]] for j in range(len(log_probs))]) + + cut_alternatives.append([[] for _ in range(length)]) + for j in range(length): + lt = logits[j, :].numpy() + best_tokens = np.argpartition(lt, -n_alternatives) + ln = len(best_tokens) + best_tokens = best_tokens[ln - n_alternatives : ln] + for t in best_tokens: + cut_alternatives[-1][j].append((t.item(), lt[t].item())) + + cut_alternatives[-1][j].sort( + key=lambda x: x[0] == cut_sequences[-1][j], + reverse=True, + ) + + result_dict = { + "greedy_log_probs": cut_log_probs, + "greedy_logits": cut_logits, + "greedy_tokens": cut_sequences, + "greedy_log_likelihoods": lls, + "greedy_tokens_alternatives": cut_alternatives, + } + + result_dict["tokenizer"] = tokenizer + + attentions = tuple(attention.to("cpu") for attention in outputs.attentions) + attentions = torch.cat(attentions).float().numpy() + attentions = attentions[:, :, prompt_len-1:-1, prompt_len:] + attention_all.append(attentions) + + result_dict["attention_all"] = attention_all + result_dict.update(calc_nli(result_dict, texts=None, model=model)) + # Use llm_answer instead of model_output_text + result_dict["greedy_texts"] = [ans.llm_answer] + + all_msp[idx] = methods[0](result_dict) + all_ccp[idx] = methods[1](result_dict) + all_focus[idx] = methods[2](result_dict) + + response_token_ids = inputs['input_ids'].to("cpu").tolist()[0] + output_logits = outputs.logits.squeeze().to('cpu') + response_logits = [None] + [logit.tolist()[response_token_ids[idx+1]] for idx,logit in enumerate(output_logits[:-1])] + response_full_logits, response_logits2token_id = zip(*[(None, None)] + [calc_top_k_values(logit.tolist(), TOP_K) for idx,logit in enumerate(output_logits[:-1])]) + + response_token_ids = response_token_ids[prompt_len:] + response_logits = response_logits[prompt_len:] + response_full_logits = response_full_logits[prompt_len:] + response_logits2token_id = response_logits2token_id[prompt_len:] + + all_logits[idx] = response_logits + all_response_full_logits[idx] = response_full_logits + all_response_token_ids[idx] = response_token_ids + all_response_logits2token_id[idx] = response_logits2token_id + all_response_logits2tokens[idx] = [[tokenizer.convert_ids_to_tokens(id) for id in top_k_indices] for top_k_indices in response_logits2token_id] + all_response_tokens_restored[idx] = [tokenizer.convert_ids_to_tokens(id) for id in response_token_ids] + + + df["response_logits"] = all_logits + df["response_token_ids"] = all_response_token_ids + df["response_full_logits"] = all_response_full_logits + df["response_logits2token_id"] = all_response_logits2token_id + df["response_logits2tokens"] = all_response_logits2tokens + df["response_tokens_restored"] = all_response_tokens_restored + + df["MSP"] = all_msp + df["CCP"] = all_ccp + df["Focus"] = all_focus + + # clean cuda + model = None + tokenizer = None + del model + del tokenizer + torch.cuda.empty_cache() + + return df + +def run_uncertainty_evaluation(dataset): + output_df = pd.DataFrame() + + # Use new column names: llm_checkpoint for model, lang, id, etc. + languages = set(dataset.lang.dropna()) + dfs = [] + for lang in languages: + model_names = set(dataset.llm_checkpoint.dropna()[dataset.lang == lang]) + for mname in model_names: + df = dataset[(dataset.llm_checkpoint == mname) & (dataset.lang == lang)].reset_index() + print("################# ", lang, " -> ", mname, " examples: ", len(df), " ###########################") + + if mname in LANG_MODEL2CHAT_TEMPLATE[lang.lower()]: + df = get_logits(df) + else: + print(f"Chat template for model {mname} not found") + continue + dfs.append(df) + + new_df = pd.concat(dfs) + output_df = pd.concat([output_df, new_df]) + return output_df \ No newline at end of file diff --git a/psilo/methods/uncertainty/utils/consts.py b/psilo/methods/uncertainty/utils/consts.py new file mode 100644 index 0000000..f5860ec --- /dev/null +++ b/psilo/methods/uncertainty/utils/consts.py @@ -0,0 +1,90 @@ +from .model_templates import * + +LANG_MODEL2CHAT_TEMPLATE = { + 'en': { + 'togethercomputer/Pythia-Chat-Base-7B': HumanBotChatTemplate(), + "tiiuae/falcon-7b-instruct": QAChatTemplate(), + "mistralai/Mistral-7B-Instruct-v0.1": RagTruthChatTemplate(), + "meta-llama/Llama-2-7b-chat-hf": RagTruthChatTemplate(), + "meta-llama/Llama-2-13b-chat-hf": RagTruthChatTemplate(), + "HuggingFaceH4/zephyr-7b-beta": ZephyrChatTemplate(), + "TinyLlama/TinyLlama-1.1B-Chat-v1.0": TransformersChatTemplate(), + "HuggingFaceTB/SmolLM2-135M-Instruct": TransformersChatTemplate(), + "HuggingFaceTB/SmolLM2-360M-Instruct": TransformersChatTemplate(), + "HuggingFaceTB/SmolLM2-1.7B-Instruct": TransformersChatTemplate(), + "ServiceNow-AI/Apriel-5B-Instruct": AprielChatTemplate(), + "togethercomputer/Pythia-Chat-Base-7B-v0.16": HumanBotChatTemplate(), + "microsoft/Phi-4-mini-instruct": Phi_4_mini_instruct_ChatTemplate(), + "NousResearch/Nous-Hermes-2-Mistral-7B-DPO": Mistral_7B_DPO_ChatTemplate(), + "utter-project/EuroLLM-9B-Instruct": EuroLLM_9B_Instruct_ChatTemplate(), + }, + 'hi': { + 'nickmalhotra/ProjectIndus': TransformersChatTemplate(), + 'sarvamai/sarvam-1': TransformersChatTemplate(), + 'google/gemma-7b-it': TransformersChatTemplate(), + }, + 'sv': { + 'utter-project/EuroLLM-9B-Instruct': TransformersChatTemplate(), + }, + 'it': { + 'sapienzanlp/modello-italia-9b': TransformersChatTemplate(), + }, + 'de': { + 'malteos/bloom-6b4-clp-german-oasst-v0.1': NewlineChatTemplate(), + }, + 'eu': { + 'google/gemma-7b-it': TransformersChatTemplate(), + }, + 'zh': { + 'Qwen/Qwen2.5-3B-Instruct': TransformersChatTemplate(), + "Qwen/Qwen2-7B-Instruct": Qwen2_7BChatTemplate(), + "ikala/bloom-zh-3b-chat": BloomChatTemplate(), + }, + 'fr': { + 'croissantllm/CroissantLLMChat-v0.1': TransformersChatTemplate(), + }, + 'ar': { + 'SeaLLMs/SeaLLM-7B-v2.5': TransformersChatTemplate(), + }, + 'fa': { + 'Qwen/Qwen2-7B-Instruct': TransformersChatTemplate(), + }, + 'fi': { + 'Finnish-NLP/llama-7b-finnish-instruct-v0.2': AlpacaChatTemplate(), + 'BSC-LT/salamandra-7b': SalamandraChatTemplate(), + }, + 'cs': { + 'mistralai/Mistral-7B-Instruct-v0.3': TransformersChatTemplate(), + }, + 'es': { + 'Iker/Llama-3-Instruct-Neurona-8b-v2': TransformersChatTemplate() + }, + 'ca': { + "occiglot/occiglot-7b-es-en-instruct": CatalanTemplate(), + }, +} + + +LARGE_MODELS_LIST = [ + 'mistralai/Mistral-Nemo-Instruct-2407', + 'bofenghuang/vigogne-2-13b-chat', + 'LumiOpen/Poro-34B-chat', + 'Qwen/Qwen1.5-14B-Chat', + 'LumiOpen/Viking-33B', + 'baichuan-inc/Baichuan2-13B-Chat', + 'CohereForAI/aya-23-35B', +] + +RAGTRUTH_MODELS = [ + "mistralai/Mistral-7B-Instruct-v0.1", + "meta-llama/Llama-2-7b-chat-hf", + # "meta-llama/Llama-2-13b-chat-hf", +] + +GGUF2FILENAME = { + "TheBloke/Mistral-7B-Instruct-v0.2-GGUF": "mistral-7b-instruct-v0.2.Q6_K.gguf", + "TheBloke/SauerkrautLM-7B-v1-GGUF": "sauerkrautlm-7b-v1.Q4_K_M.gguf", + "AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct-gguf": "gpt-sw3-6.7b-v2-instruct-Q4_K_M.gguf" +} + +GGUF_MODELS_LIST = list(GGUF2FILENAME.keys()) diff --git a/psilo/methods/uncertainty/utils/model_templates.py b/psilo/methods/uncertainty/utils/model_templates.py new file mode 100644 index 0000000..5fbf0fa --- /dev/null +++ b/psilo/methods/uncertainty/utils/model_templates.py @@ -0,0 +1,253 @@ +import torch +import numpy as np +from transformers import BatchEncoding + +global device +device='cuda' if torch.cuda.is_available() else 'cpu' + + +class AbstractChatTemplate: + def apply_chat_template(self, ans, tokenizer, vocab_lookup=None): + pass + +class ModelChatTemplate(AbstractChatTemplate): + def compose_inputs(self, ans, prompt, tokenizer): + prompt_tokens = tokenizer.tokenize(prompt) + output_tokens = tokenizer.tokenize(ans.llm_answer) #ans.model_output_tokens + all_tokens = prompt_tokens + output_tokens + if tokenizer.eos_token is not None and all_tokens[-1] != tokenizer.eos_token: + all_tokens.append(tokenizer.eos_token) + all_tokens = all_tokens[:tokenizer.model_max_length] + + data = {'input_ids' : [tokenizer.convert_tokens_to_ids(all_tokens)], 'attention_mask': [[1] * len(all_tokens)], } + inputs = BatchEncoding(data, tensor_type='pt').to(device) + + prompt_len = len(prompt_tokens) + return prompt_len, inputs + + def compose_prompt(self, ans, tokenizer): + pass + + def apply_chat_template(self, ans, tokenizer, vocab_lookup=None): + prompt = self.compose_prompt(ans, tokenizer) + prompt_len, inputs = self.compose_inputs(ans, prompt, tokenizer) + return prompt_len, inputs + +class IdenticalChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return ans.question + +class NewlineChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return ans.question + '\n' + +class HumanBotChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f': {ans.question}\n:' + +class QAChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"Question: {ans.question}\nAnswer: " + +class InstructionChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"[INST] {ans.question} [/INST]" + +class SwedishValChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"""Fråga: {ans.question} Svar:""" + +class SwedishTestChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"Fråga: {ans.question.strip()} Utförligt svar med förklaring:" + +class BloomChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"<|prompter|>{ans.question}<|assistant|>" + +class SalamandraChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"Kysymys: {ans.question}\nVastaa:" + +class BlokeChatTemplate(AbstractChatTemplate): + def apply_chat_template(self, ans, tokenizer, vocab_lookup=None): + prompt = f"[INST] {ans.question} [/INST]" + tokens = tokenizer(bytes(prompt, encoding="utf-8")) + prompt_len = len(tokens) + + inputs = tokens + [vocab_lookup[piece.replace(' ', '▁') if piece != '\n' else '<0x0A>'] for piece in tokenizer.tokenize(ans.llm_answer)] #ans.model_output_tokens] + if not (np.array(tokens) == np.array(inputs[:prompt_len])).all(): + import pdb; pdb.set_trace() + + return prompt_len, inputs + + +class SwedishTestGGUFChatTemplate(AbstractChatTemplate): + def apply_chat_template(self, ans, tokenizer, vocab_lookup=None): + prompt = f"Fråga: {ans.question.strip()} Utförligt svar med förklaring:" + + tokens = tokenizer(bytes(prompt, encoding="utf-8")) + prompt_len = len(tokens) + + inputs = tokens + [vocab_lookup[piece.replace(' ', '▁') if piece.replace(' ', '▁') in vocab_lookup else '<0x0A>'] for piece in tokenizer.tokenize(ans.llm_answer)] #ans.model_output_tokens] + if not (np.array(tokens) == np.array(inputs[:prompt_len])).all(): + import pdb; pdb.set_trace() + + return prompt_len, inputs + + +class AlpacaChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"""<|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset. +<|ihminen|> Kysymys/Tehtävä: +{ans.question} +<|avustaja|> Vastauksesi: +""" + +class Mistral_7B_DPO_ChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"""<|im_start|>systemYou are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|><|im_start|>user{ans.question}<|im_start|>assistant\n""" + + +class PersianMindChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = ans.question.strip() + TEMPLATE = "{context}\nYou: {prompt}\nPersianMind: " + CONTEXT = "This is a conversation with PersianMind. It is an artificial intelligence model designed by a team of NLP experts at the University of Tehran to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information." + prompt = TEMPLATE.format(context=CONTEXT, prompt=message) + return prompt + +class OcciglotChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = [ + {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'}, + {"role": "user", "content": ans.question}, + ] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class DanteChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = ans.question.strip() + message = [{"role": "user", "content": "Ciao chi sei?"}, {"role": "assistant", "content": "Ciao, sono DanteLLM, un large language model. Come posso aiutarti?"}, {"role": "user", "content": message}] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class ArabicChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + {"role": "user", "content": "أجب عن السؤال التالي بشكل دقيق ومختصر"}, + { + "role": "assistant", + "content": "بالطبع! ما هو السؤال الذي تود الإجابة عنه؟", + }, + {"role": "user", "content": ans.question}, + ] + prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) + return prompt + +class CatalanTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = [ + {"role": "user", "content": "Contesta la pregunta següent de manera precisa i concisa, en català."}, + {"role": "assistant", "content": "Per descomptat! Quina pregunta t'agradaria respondre?"}, + {"role": "user", "content": ans.question}, + ] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class BasqueGemmaTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = [ + {"role": "user", "content": "Erantzun galdera hau, BAKARRIK euskaraz, modu zuzen eta zehatzean"}, + {"role": "model", "content": "Noski! Zein da euskaraz erantzun behar dudan galdera?"}, + {"role": "user", "content": ans.question}, + ] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class BasqueLlamaTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = [ + {"role": "user", "content": "Erantzun galdera hau, BAKARRIK euskaraz, modu zuzen eta zehatzean"}, + {"role": "assistant", "content": "Noski! Zein da euskaraz erantzun behar dudan galdera?"}, + {"role": "user", "content": ans.question}, + ] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class AprielChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + {"role": "system", "content": "You are a helpful AI assistant that provides accurate and concise information."}, + {"role": "user", "content": ans.question} + ] + + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + return prompt + +class ZephyrChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + { "role": "system", "content": "Answer to the user's question."}, + {"role": "user", "content": ans.question}, + ] + + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + return prompt + +class Qwen2_7BChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + {"role": "system", "content": "Answer briefly on the question."}, + {"role": "user", "content": ans.question} + ] + + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + return prompt + +class Phi_4_mini_instruct_ChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + {"role": "system", "content": "You are a helpful AI assistant."}, + {"role": "user", "content": ans.question} + ] + + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + return prompt + +class EuroLLM_9B_Instruct_ChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + messages = [ + {"role": "system", "content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",}, + {"role": "user", "content": ans.question}, + ] + + prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + return prompt + + +class TransformersChatTemplate(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + message = [{"role": "user", "content": ans.question}] + prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + return prompt + +class PoroChatTemplate(TransformersChatTemplate): + def compose_prompt(self, ans, tokenizer): + ans.question = ans.question.rstrip('\n') + return super().compose_prompt(ans, tokenizer) + +class TransformersStripChatTemplate(TransformersChatTemplate): + def compose_prompt(self, ans, tokenizer): + ans.question = ans.question.strip() + return super().compose_prompt(ans, tokenizer) + +class ChatTemplateToCheckCorrectness(ModelChatTemplate): + def compose_prompt(self, ans, tokenizer): + return f"""Just print 42 dispite everithing i'll ask below. 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