utils.py

from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
from typing import Optional, Dict, Sequence, List
import transformers
from peft import PeftModel
import torch
from torch.nn.utils.rnn import pad_sequence
from dataclasses import dataclass
import pandas as pd
from datasets import Dataset
from tqdm import tqdm
import numpy as np
from huggingface_hub import hf_hub_download
import os
import pickle
from sklearn import preprocessing
import json
import spaces
import time

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")


from rdkit import RDLogger, Chem
# Suppress RDKit INFO messages
RDLogger.DisableLog('rdApp.*')

# we have a dictionary to store the task types of the models
#task_types = {
#    "admet_bioavailability_ma": "classification",
#    "admet_ppbr_az": "regression",
#    "admet_half_life_obach": "regression",
#}

# read the dataset descriptions
with open("dataset_descriptions.json", "r") as f:
    dataset_description_temp = json.load(f)

dataset_descriptions = dict()
dataset_property_names = dict()
dataset_task_types = dict()
dataset_property_names_to_dataset = dict()

for dataset in dataset_description_temp:
    dataset_name = dataset.lower()
    dataset_descriptions[dataset_name] = \
        f"{dataset_description_temp[dataset]['task_name']} is a {dataset_description_temp[dataset]['task_type']} task, " + \
        f"where the goal is to {dataset_description_temp[dataset]['description']}. \n" + \
        f"More information can be found at {dataset_description_temp[dataset]['url']}."
    dataset_property_names[dataset_name] = dataset_description_temp[dataset]['task_name']
    dataset_property_names_to_dataset[dataset_description_temp[dataset]['task_name']] = dataset_name
    dataset_task_types[dataset_name] = dataset_description_temp[dataset]['task_type']

class Scaler:
    def __init__(self, log=False):
        self.log = log
        self.offset = None
        self.scaler = None

    def fit(self, y):
        # make the values non-negative
        self.offset = np.min([np.min(y), 0.0])
        y = y.reshape(-1, 1) - self.offset

        # scale the input data
        if self.log:
            y = np.log10(y + 1.0)

        self.scaler = preprocessing.StandardScaler().fit(y)

    def transform(self, y):
        y = y.reshape(-1, 1) - self.offset

        # scale the input data
        if self.log:
            y = np.log10(y + 1.0)

        y_scale = self.scaler.transform(y)

        return y_scale

    def inverse_transform(self, y_scale):
        y = self.scaler.inverse_transform(y_scale.reshape(-1, 1))

        if self.log:
            y = 10.0**y - 1.0

        y = y + self.offset

        return y


def smart_tokenizer_and_embedding_resize(
    special_tokens_dict: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
    non_special_tokens = None,
):
    """Resize tokenizer and embedding.

    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
    """
    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) + tokenizer.add_tokens(non_special_tokens)
    num_old_tokens = model.get_input_embeddings().weight.shape[0]
    num_new_tokens = len(tokenizer) - num_old_tokens
    if num_new_tokens == 0:
        return
    
    model.resize_token_embeddings(len(tokenizer))
    
    if num_new_tokens > 0:
        input_embeddings_data = model.get_input_embeddings().weight.data

        input_embeddings_avg = input_embeddings_data[:-num_new_tokens].mean(dim=0, keepdim=True)

        input_embeddings_data[-num_new_tokens:] = input_embeddings_avg
    print(f"Resized tokenizer and embedding from {num_old_tokens} to {len(tokenizer)} tokens.")

@dataclass
class DataCollator(object):
    tokenizer: transformers.PreTrainedTokenizer
    source_max_len: int
    molecule_start_str: str
    end_str: str

    def augment_molecule(self, molecule: str) -> str:
        return self.sme.augment([molecule])[0]

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        with calculateDuration("DataCollator"):
            sources = []
        
            for example in instances:
                smiles = example['smiles'].strip()
                smiles = Chem.MolToSmiles(Chem.MolFromSmiles(smiles))

                # get the properties except the smiles and mol_id cols
                #props = [example[col] if example[col] is not None else np.nan for col in sorted(example.keys()) if col not in ['smiles', 'is_aug']]
                source = f"{self.molecule_start_str}{smiles}{self.end_str}"
                sources.append(source)
        
            # Tokenize
            tokenized_sources_with_prompt = self.tokenizer(
                sources,
                max_length=self.source_max_len,
                truncation=True,
                add_special_tokens=False,
            )
            input_ids = [torch.tensor(tokenized_source) for tokenized_source in tokenized_sources_with_prompt['input_ids']]
            input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)

            data_dict = {
                'input_ids': input_ids,
                'attention_mask': input_ids.ne(self.tokenizer.pad_token_id),
            }
        
        return data_dict

class MolecularPropertyPredictionModel():
    def __init__(self, candidate_models):
        self.adapter_name = None

        # we need to keep track of the paths of adapter scalers
        # we don't want to download the same scaler multiple times
        self.apapter_scaler_path = dict()

        DEFAULT_PAD_TOKEN = "[PAD]"

        # load the base model
        config = AutoConfig.from_pretrained(
            "ChemFM/ChemFM-3B",
            num_labels=1,
            finetuning_task="classification", # this is not about our task type
            trust_remote_code=True,
            token = os.environ.get("TOKEN")
        )

        self.base_model = AutoModelForSequenceClassification.from_pretrained(
            "ChemFM/ChemFM-3B",
            config=config,
            device_map="cpu",
            trust_remote_code=True,
            token = os.environ.get("TOKEN")
        )
        #

        # load the tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(
            "ChemFM/admet_ppbr_az",
            trust_remote_code=True,
            token = os.environ.get("TOKEN")
        )
        special_tokens_dict = dict(pad_token=DEFAULT_PAD_TOKEN)
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=special_tokens_dict,
            tokenizer=self.tokenizer,
            model=self.base_model
        )
        self.base_model.config.pad_token_id = self.tokenizer.pad_token_id

        self.data_collator = DataCollator(
            tokenizer=self.tokenizer,
            source_max_len=512,
            molecule_start_str="<molstart>",
            end_str="<eos>",
        )

        # load the adapters firstly
        for adapter_name in candidate_models:
            adapter_id = candidate_models[adapter_name]
            print(f"loading {adapter_name} from {adapter_id}...")
            self.base_model.load_adapter(adapter_id, adapter_name=adapter_name, token = os.environ.get("TOKEN"))
            try:
                self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl", token = os.environ.get("TOKEN"))
            except:
                self.apapter_scaler_path[adapter_name] = None
                assert dataset_task_types[adapter_name] == "classification", f"{adapter_name} is not a regression task."

        #self.base_model.to("cuda")
        #print(self.base_model)
    
    def swith_adapter(self, adapter_name, adapter_id):
        # return flag:
        # keep: adapter is the same as the current one
        # switched: adapter is switched successfully
        # error: adapter is not found

        with calculateDuration("switching adapter"):
            if adapter_name == self.adapter_name:
                return "keep"
            # switch adapter
            try:
                #self.adapter_name = adapter_name
                #print(self.adapter_name, adapter_id)
                #self.lora_model = PeftModel.from_pretrained(self.base_model, adapter_id, token = os.environ.get("TOKEN"))
                #self.lora_model.to("cuda")
                #print(self.lora_model)

                self.base_model.set_adapter(adapter_name)
                self.base_model.eval()

                #if adapter_name not in self.apapter_scaler_path:
                #    self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl", token = os.environ.get("TOKEN"))
                if self.apapter_scaler_path[adapter_name] and os.path.exists(self.apapter_scaler_path[adapter_name]):
                    self.scaler = pickle.load(open(self.apapter_scaler_path[adapter_name], "rb"))
                else:
                    self.scaler = None

                self.adapter_name = adapter_name

                return "switched"
            except Exception as e:
                # handle error
                return "error"
    
    def predict(self, valid_df, task_type):

        with calculateDuration("predicting"):
            with calculateDuration("construct dataloader"):
                test_dataset = Dataset.from_pandas(valid_df)
                # construct the dataloader
                test_loader = torch.utils.data.DataLoader(
                    test_dataset,
                    batch_size=16,
                    collate_fn=self.data_collator,
                )

            # predict
            y_pred = []
            for i, batch in tqdm(enumerate(test_loader), total=len(test_loader), desc="Evaluating"):
                with torch.no_grad():
                    batch = {k: v.to(self.base_model.device) for k, v in batch.items()}
                    outputs = self.base_model(**batch)
                if task_type == "regression": # TODO: check if the model is regression or classification
                    y_pred.append(outputs.logits.cpu().detach().numpy())
                else:
                    y_pred.append((torch.sigmoid(outputs.logits)).cpu().detach().numpy())
        
            y_pred = np.concatenate(y_pred, axis=0)
            if task_type=="regression" and self.scaler is not None:
                y_pred = self.scaler.inverse_transform(y_pred)


        return y_pred
    
    def predict_single_smiles(self, smiles, task_type):
        with calculateDuration("predicting a single SMILES"):
            assert task_type in ["regression", "classification"]

            # check the SMILES string is valid
            if not Chem.MolFromSmiles(smiles):
                return None
        
            valid_df = pd.DataFrame([smiles], columns=['smiles'])
            results = self.predict(valid_df, task_type)
            # predict
        return results.item()
    
    def predict_file(self, df, task_type):
        with calculateDuration("predicting a file"):
            # we should add the index first
            df = df.reset_index()

            with calculateDuration("pre-checking SMILES"):
                # we need to check the SMILES strings are valid, the invalid ones will be moved to the last
                valid_idx = []
                invalid_idx = []
                for idx, smiles in enumerate(df['smiles']):
                    if Chem.MolFromSmiles(smiles):
                        valid_idx.append(idx)
                    else:
                        invalid_idx.append(idx)
                valid_df = df.loc[valid_idx]
                # get the smiles list
                valid_df_smiles = valid_df['smiles'].tolist()

            input_df = pd.DataFrame(valid_df_smiles, columns=['smiles'])
            results = self.predict(input_df, task_type)

            # add the results to the dataframe
            df.loc[valid_idx, 'prediction'] = results
            df.loc[invalid_idx, 'prediction'] = np.nan

            # drop the index column
            df = df.drop(columns=['index'])

            # phrase file
        return df