models/smi_ted/inference/smi_ted_large/load.py

PATTERN = "(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9])"
# Deep learning
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn

# Transformers
from fast_transformers.attention import AttentionLayer
from fast_transformers.events import QKVEvent
from fast_transformers.transformers import TransformerEncoder, TransformerEncoderLayer
from fast_transformers.builders.transformer_builders import BaseTransformerEncoderBuilder
from fast_transformers.builders.attention_builders import AttentionBuilder
from fast_transformers.feature_maps import GeneralizedRandomFeatures
from fast_transformers.masking import LengthMask
from transformers import BertTokenizer

# Data
import numpy as np
import pandas as pd

# Chemistry
from rdkit import Chem
from rdkit.Chem import PandasTools
from rdkit.Chem import Descriptors
PandasTools.RenderImagesInAllDataFrames(True)

# Standard library
from functools import partial
import regex as re
import random
import os
import gc
from tqdm import tqdm
tqdm.pandas()


# function to canonicalize SMILES
def normalize_smiles(smi, canonical=True, isomeric=False):
    try:
        normalized = Chem.MolToSmiles(
            Chem.MolFromSmiles(smi), canonical=canonical, isomericSmiles=isomeric
        )
    except:
        normalized = None
    return normalized


class MolTranBertTokenizer(BertTokenizer):
    def __init__(self, vocab_file: str = '',
                 do_lower_case=False,
                 unk_token='<pad>',
                 sep_token='<eos>',
                 pad_token='<pad>',
                 cls_token='<bos>',
                 mask_token='<mask>',
                 **kwargs):
        super().__init__(vocab_file,
                         unk_token=unk_token,
                         sep_token=sep_token,
                         pad_token=pad_token,
                         cls_token=cls_token,
                         mask_token=mask_token,
                         **kwargs)

        self.regex_tokenizer = re.compile(PATTERN)
        self.wordpiece_tokenizer = None
        self.basic_tokenizer = None
        with open(vocab_file) as f:
            self.padding_idx = f.readlines().index(pad_token+'\n')

    def _tokenize(self, text):
        split_tokens = self.regex_tokenizer.findall(text)
        return split_tokens
    
    def convert_idx_to_tokens(self, idx_tensor):
        tokens = [self.convert_ids_to_tokens(idx) for idx in idx_tensor.tolist()]
        return tokens

    def convert_tokens_to_string(self, tokens):
        stopwords = ['<bos>', '<eos>']
        clean_tokens = [word for word in tokens if word not in stopwords]
        out_string = ''.join(clean_tokens)
        return out_string
    
    def get_padding_idx(self):
        return self.padding_idx
    
    def idx_to_smiles(self, torch_model, idx):
        '''Convert tokens idx back to SMILES text'''
        rev_tokens = torch_model.tokenizer.convert_idx_to_tokens(idx)
        flat_list_tokens = [item for sublist in rev_tokens for item in sublist]
        decoded_smiles = torch_model.tokenizer.convert_tokens_to_string(flat_list_tokens)
        return decoded_smiles


## Transformer layers
class RotaryEmbedding(torch.nn.Module):
    
    def __init__(self, dim, base=10000):
        super().__init__()
        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)
        self.seq_len_cached = 0 
        self.cos_cached = None
        self.sin_cached = None

    def forward(self, x, seq_dim=1):
        seq_len = x.shape[seq_dim]
        if seq_len != self.seq_len_cached:
            self.seq_len_cached = seq_len
            
            t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
            
            self.cos_cached = emb.cos()[None,:, None, :]
            self.sin_cached = emb.sin()[None,:, None, :]
                
        return self.cos_cached, self.sin_cached

def rotate_half(x):
    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
    return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions

@torch.jit.script
def apply_rotary_pos_emb(q, k, cos, sin):
    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)

class RotateAttentionLayer(AttentionLayer):
    """Rotate attention layer inherits from fast_transformer attention layer. 
        The only thing added is an Embedding encoding, for more information
        on the attention layer see the fast_transformers code
    """
    def __init__(self, attention, d_model, n_heads, d_keys=None,
                 d_values=None, event_dispatcher=""):
        super(RotateAttentionLayer, self).__init__(attention,d_model, n_heads, d_keys=d_keys,
                 d_values=d_values, event_dispatcher=event_dispatcher)

        self.rotaryemb = RotaryEmbedding(d_keys)
        print('Using Rotation Embedding')

    def forward(self, queries, keys, values, attn_mask, query_lengths,
                key_lengths):
        """
        Using the same frame work as the fast_Transformers attention layer
        but injecting rotary information to the queries and the keys
        after the keys and queries are projected. 
        In the argument description we make use of the following sizes
            - N: the batch size
            - L: The maximum length of the queries
            - S: The maximum length of the keys (the actual length per sequence
              is given by the length mask)
            - D: The input feature dimensionality passed in the constructor as
              'd_model'
        Arguments
        ---------
            queries: (N, L, D) The tensor containing the queries
            keys: (N, S, D) The tensor containing the keys
            values: (N, S, D) The tensor containing the values
            attn_mask: An implementation of BaseMask that encodes where each
                       query can attend to
            query_lengths: An implementation of  BaseMask that encodes how
                           many queries each sequence in the batch consists of
            key_lengths: An implementation of BaseMask that encodes how
                         many queries each sequence in the batch consists of
        Returns
        -------
            The new value for each query as a tensor of shape (N, L, D).
        """
        # Extract the dimensions into local variables
        N, L, _ = queries.shape
        _, S, _ = keys.shape
        H = self.n_heads

        # Project the queries/keys/values
        queries = self.query_projection(queries).view(N, L, H, -1)
        keys = self.key_projection(keys).view(N, S, H, -1)
        cos, sin = self.rotaryemb(queries)
        queries, keys = apply_rotary_pos_emb(queries, keys, cos, sin)
        values = self.value_projection(values).view(N, S, H, -1)
        # Let the world know of the qkv
        self.event_dispatcher.dispatch(QKVEvent(self, queries, keys, values))


        # Compute the attention
        new_values = self.inner_attention(
            queries,
            keys,
            values,
            attn_mask,
            query_lengths,
            key_lengths
        ).view(N, L, -1)

        # Project the output and return
        return self.out_projection(new_values)

class RotateEncoderBuilder(BaseTransformerEncoderBuilder):
    """Build a batch transformer encoder with Relative Rotary embeddings
    for training or processing of sequences all elements at a time.
    Example usage:
        builder = RotateEncoderBuilder()
        builder.n_layers = 12
        builder.n_heads = 8
        builder.feed_forward_dimensions = 1024
        builder.query_dimensions = 64
        builder.value_dimensions = 64
        builder.dropout = 0.1
        builder.attention_dropout = 0.1
        builder.attention_type = "linear"
        transformer = builder.get()
    """
    def _get_attention_builder(self):
        """Return an instance of the appropriate attention builder."""
        return AttentionBuilder()

    def _get_attention_layer_class(self):
        """Return the class for the layer that projects queries keys and
        values."""
        return RotateAttentionLayer

    def _get_encoder_class(self):
        """Return the class for the transformer encoder."""
        return TransformerEncoder

    def _get_encoder_layer_class(self):
        """Return the class for the transformer encoder layer."""
        return TransformerEncoderLayer


class AutoEncoderLayer(nn.Module):

    def __init__(self, feature_size, latent_size):
        super().__init__()
        self.encoder = self.Encoder(feature_size, latent_size)
        self.decoder = self.Decoder(feature_size, latent_size)
    
    class Encoder(nn.Module):

        def __init__(self, feature_size, latent_size):
            super().__init__()
            self.is_cuda_available = torch.cuda.is_available()
            self.fc1 = nn.Linear(feature_size, latent_size)
            self.ln_f = nn.LayerNorm(latent_size)
            self.lat = nn.Linear(latent_size, latent_size, bias=False)
        
        def forward(self, x):
            if self.is_cuda_available:
                self.fc1.cuda()
                self.ln_f.cuda()
                self.lat.cuda()
                x = x.cuda()
            x = F.gelu(self.fc1(x))
            x = self.ln_f(x)
            x = self.lat(x)
            return x # -> (N, D)
    
    class Decoder(nn.Module):

        def __init__(self, feature_size, latent_size):
            super().__init__()
            self.is_cuda_available = torch.cuda.is_available()
            self.fc1 = nn.Linear(latent_size, latent_size)
            self.ln_f = nn.LayerNorm(latent_size)
            self.rec = nn.Linear(latent_size, feature_size, bias=False)
        
        def forward(self, x):
            if self.is_cuda_available:
                self.fc1.cuda()
                self.ln_f.cuda()
                self.rec.cuda()
                x = x.cuda()
            x = F.gelu(self.fc1(x))
            x = self.ln_f(x)
            x = self.rec(x)
            return x # -> (N, L*D)


class LangLayer(nn.Module):

    def __init__(self, n_embd, n_vocab):
        super().__init__()
        self.is_cuda_available = torch.cuda.is_available()
        self.embed = nn.Linear(n_embd, n_embd)
        self.ln_f = nn.LayerNorm(n_embd)
        self.head = nn.Linear(n_embd, n_vocab, bias=False)
    
    def forward(self, tensor):
        if self.is_cuda_available:
            self.embed.cuda()
            self.ln_f.cuda()
            self.head.cuda()
            tensor = tensor.cuda()
        tensor = self.embed(tensor)
        tensor = F.gelu(tensor)
        tensor = self.ln_f(tensor)
        tensor = self.head(tensor)
        return tensor
    

class Net(nn.Module):
    
    def __init__(self, smiles_embed_dim, n_output=1, dropout=0.2):
        super().__init__()
        self.desc_skip_connection = True
        self.fc1 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.relu1 = nn.GELU()
        self.fc2 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
        self.dropout2 = nn.Dropout(dropout)
        self.relu2 = nn.GELU()
        self.final = nn.Linear(smiles_embed_dim, n_output)

    def forward(self, smiles_emb, multitask=False):
        x_out = self.fc1(smiles_emb)
        x_out = self.dropout1(x_out)
        x_out = self.relu1(x_out)

        if self.desc_skip_connection is True:
            x_out = x_out + smiles_emb

        z = self.fc2(x_out)
        z = self.dropout2(z)
        z = self.relu2(z)
        if self.desc_skip_connection is True:
            z = self.final(z + x_out)
        else:
            z = self.final(z)

        if multitask:
            return F.sigmoid(z)
        return z


class MoLEncoder(nn.Module):

    def __init__(self, config, n_vocab):
        super(MoLEncoder, self).__init__()

        # embeddings
        self.config = config
        self.tok_emb = nn.Embedding(n_vocab, config['n_embd'])
        self.drop = nn.Dropout(config['d_dropout'])

        # transformer
        builder = RotateEncoderBuilder.from_kwargs(
            n_layers=config['n_layer'],
            n_heads=config['n_head'],
            query_dimensions=config['n_embd']//config['n_head'],
            value_dimensions=config['n_embd']//config['n_head'],
            feed_forward_dimensions=None,
            attention_type='linear',
            # unless we do deterministic_eval here, we will have random outputs
            feature_map=partial(GeneralizedRandomFeatures, 
                                n_dims=config['num_feats'], 
                                deterministic_eval=True),
            activation='gelu'
        )
        self.blocks = builder.get()

        # classification
        self.lang_model = LangLayer(config['n_embd'], n_vocab)

    def forward(self, idx, mask):
        # transformer encoder
        x = self.tok_emb(idx) # each index maps to a (learnable) vector
        x = self.drop(x)
        x = self.blocks(x, length_mask=LengthMask(mask.sum(-1), max_len=idx.shape[1]))

        # add padding
        token_embeddings = x
        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        mask_embeddings = (token_embeddings * input_mask_expanded)
        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.config['max_len'] - mask_embeddings.shape[1]), value=0)

        return token_embeddings


class MoLDecoder(nn.Module):

    def __init__(self, n_vocab, max_len, n_embd, n_gpu=None):
        super(MoLDecoder, self).__init__()

        self.max_len = max_len
        self.n_embd = n_embd
        self.n_gpu = n_gpu
        self.autoencoder = AutoEncoderLayer(n_embd*max_len, n_embd)
        self.lang_model = LangLayer(n_embd, n_vocab)


class Smi_ted(nn.Module):
    """materials.smi-ted-Large 738M Parameters"""

    def __init__(self, tokenizer, config=None):
        super(Smi_ted, self).__init__()

        # configuration
        self.config = config
        self.tokenizer = tokenizer
        self.padding_idx = tokenizer.get_padding_idx()
        self.n_vocab = len(self.tokenizer.vocab)
        self.is_cuda_available = torch.cuda.is_available()

        # instantiate modules
        if self.config:
            self.encoder = MoLEncoder(self.config, self.n_vocab)
            self.decoder = MoLDecoder(self.n_vocab, self.config['max_len'], self.config['n_embd'])
            self.net = Net(self.config['n_embd'], n_output=self.config['n_output'], dropout=self.config['d_dropout'])
        
    def load_checkpoint(self, ckpt_path):
        # load checkpoint file
        checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))

        # load hyparameters
        self.config = checkpoint['hparams']
        self.max_len = self.config['max_len']
        self.n_embd = self.config['n_embd']
        self._set_seed(self.config['seed'])

        # instantiate modules
        self.encoder = MoLEncoder(self.config, self.n_vocab)
        self.decoder = MoLDecoder(self.n_vocab, self.max_len, self.n_embd)
        self.net = Net(self.n_embd, n_output=self.config['n_output'] if 'n_output' in self.config else 1, dropout=self.config['d_dropout'])

        # load weights
        if 'state_dict' in checkpoint:
            if isinstance(checkpoint['state_dict'], list):
                self.encoder.load_state_dict(checkpoint['state_dict'][0], strict=False)
                self.decoder.load_state_dict(checkpoint['state_dict'][1], strict=False)
            else:
                self.load_state_dict(checkpoint['state_dict'], strict=False)
        elif 'MODEL_STATE' in checkpoint:
            self.load_state_dict(checkpoint['MODEL_STATE'], strict=False)

        # load RNG states each time the model and states are loaded from checkpoint
        if 'rng' in self.config:
            rng = self.config['rng']
            for key, value in rng.items():
                if key =='torch_state':
                    torch.set_rng_state(value.cpu())
                elif key =='cuda_state':
                    torch.cuda.set_rng_state(value.cpu())
                elif key =='numpy_state':
                    np.random.set_state(value)
                elif key =='python_state':
                    random.setstate(value)
                else:
                    print('unrecognized state')

    def _set_seed(self, value):
        print('Random Seed:', value)
        random.seed(value)
        torch.manual_seed(value)
        torch.cuda.manual_seed(value)
        torch.cuda.manual_seed_all(value)
        np.random.seed(value)
        cudnn.deterministic = True
        cudnn.benchmark = False

    def forward(self, smiles, batch_size=100):
        return self.decode(self.encode(smiles, batch_size=batch_size, return_torch=True))
            
    def tokenize(self, smiles):
        """Tokenize a string into tokens."""
        if isinstance(smiles, str):
            batch = [smiles]
        else:
            batch = smiles
        
        tokens = self.tokenizer(
            batch, 
            padding=True,
            truncation=True, 
            add_special_tokens=True, 
            return_tensors="pt",
            max_length=self.max_len,
        )
        
        idx = tokens['input_ids'].clone().detach()
        mask = tokens['attention_mask'].clone().detach()

        if self.is_cuda_available:
            return idx.cuda(), mask.cuda()
        
        return idx, mask
    
    def extract_all(self, smiles):
        """Extract all elements from each part of smi-ted. Be careful."""
        # evaluation mode
        self.encoder.eval()
        self.decoder.eval()
        if self.is_cuda_available:
            self.encoder.cuda()
            self.decoder.cuda()

        # handle single str or a list of str
        smiles = pd.Series(smiles) if isinstance(smiles, str) else pd.Series(list(smiles))

        # SMILES normalization
        smiles = smiles.apply(normalize_smiles)
        null_idx = smiles[smiles.isnull()].index.to_list()  # keep track of SMILES that cannot normalize
        smiles = smiles.dropna()
        
        # tokenizer
        idx, mask = self.tokenize(smiles.to_list())
        
        ###########
        # Encoder #
        ###########
        # encoder forward
        x = self.encoder.tok_emb(idx) # each index maps to a (learnable) vector
        x = self.encoder.drop(x)
        x = self.encoder.blocks(x, length_mask=LengthMask(mask.sum(-1)))

        # mean pooling
        token_embeddings = x
        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
        sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
        true_set = sum_embeddings / sum_mask # DO NOT USE THIS FOR DOWNSTREAM TASKS, USE `pred_set` INSTEAD

        # add padding
        mask_embeddings = (token_embeddings * input_mask_expanded)
        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.max_len - mask_embeddings.shape[1]), value=0)
        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=2)

        true_ids = idx
        true_cte = token_embeddings
        true_cte = true_cte.view(-1, self.max_len*self.n_embd)

        ###########
        # Decoder #
        ###########
        # CTE autoencoder
        pred_set = self.decoder.autoencoder.encoder(true_cte)
        pred_cte = self.decoder.autoencoder.decoder(pred_set)

        # reconstruct tokens
        pred_ids = self.decoder.lang_model(pred_cte.view(-1, self.max_len, self.n_embd))
        pred_ids = torch.argmax(pred_ids, axis=-1)

        # replacing null SMILES with NaN values
        for idx in null_idx:
            true_ids = true_ids.tolist()
            pred_ids = pred_ids.tolist()
            true_cte = true_cte.tolist()
            pred_cte = pred_cte.tolist()
            true_set = true_set.tolist()
            pred_set = pred_set.tolist()

            true_ids.insert(idx, np.array([np.nan]*self.config['max_len']))
            pred_ids.insert(idx, np.array([np.nan]*self.config['max_len']))
            true_cte.insert(idx, np.array([np.nan] * (self.config['max_len']*self.config['n_embd'])))
            pred_cte.insert(idx, np.array([np.nan] * (self.config['max_len']*self.config['n_embd'])))
            true_set.insert(idx, np.array([np.nan]*self.config['n_embd']))
            pred_set.insert(idx, np.array([np.nan]*self.config['n_embd']))
        
        if len(null_idx) > 0:
            true_ids = torch.tensor(true_ids)
            pred_ids = torch.tensor(pred_ids)
            true_cte = torch.tensor(true_cte)
            pred_cte = torch.tensor(pred_cte)
            true_set = torch.tensor(true_set)
            pred_set = torch.tensor(pred_set)
        
        return ((true_ids, pred_ids), # tokens
               (true_cte, pred_cte),  # token embeddings
               (true_set, pred_set))  # smiles embeddings

    def extract_embeddings(self, smiles):
        """Extract token and SMILES embeddings."""
        # evaluation mode
        self.encoder.eval()
        if self.is_cuda_available:
            self.encoder.cuda()
        
        # tokenizer
        idx, mask = self.tokenize(smiles)
        
        # encoder forward
        token_embeddings = self.encoder(idx, mask)

        # aggregate token embeddings (similar to mean pooling)
        # CAUTION: use the embeddings from the autoencoder.
        smiles_embeddings = self.decoder.autoencoder.encoder(token_embeddings.view(-1, self.max_len*self.n_embd))

        # add padding
        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=self.padding_idx)

        return idx, token_embeddings, smiles_embeddings
    
    def encode(self, smiles, useCuda=False, batch_size=100, return_torch=False):
        """Extract efficiently SMILES embeddings per batches."""
        # TODO: remove useCuda argument

        # handle single str or a list of str
        smiles = pd.Series(smiles) if isinstance(smiles, str) else pd.Series(list(smiles))

        # SMILES normalization
        smiles = smiles.apply(normalize_smiles)
        null_idx = smiles[smiles.isnull()].index.to_list()  # keep track of SMILES that cannot normalize
        smiles = smiles.dropna()

        # process in batches
        n_split = smiles.shape[0] // batch_size if smiles.shape[0] >= batch_size else smiles.shape[0]
        embeddings = [
            self.extract_embeddings(list(batch))[2].cpu().detach().numpy() 
                for batch in tqdm(np.array_split(smiles, n_split))
        ]
        flat_list = [item for sublist in embeddings for item in sublist]

        # clear GPU memory
        if self.is_cuda_available:
            torch.cuda.empty_cache()
            gc.collect()

        # replacing null SMILES with NaN values
        for idx in null_idx:
            flat_list.insert(idx, np.array([np.nan]*self.config['n_embd']))
        flat_list = np.asarray(flat_list)

        if return_torch:
            return torch.tensor(flat_list)
        return pd.DataFrame(flat_list)
    
    def decode(self, smiles_embeddings):
        """Decode SMILES embeddings back to SMILES."""
        # evaluation mode
        self.decoder.eval()
        if self.is_cuda_available:
            self.decoder.cuda()

        # reconstruct token embeddings
        pred_token_embds = self.decoder.autoencoder.decoder(smiles_embeddings)

        # reconstruct tokens
        pred_idx = self.decoder.lang_model(pred_token_embds.view(-1, self.max_len, self.n_embd))
        pred_idx = torch.argmax(pred_idx, axis=-1).cpu().detach().numpy()

        # convert idx to tokens
        pred_smiles = []
        for i in range(pred_idx.shape[0]):
            idx = pred_idx[i]
            smiles = self.tokenizer.idx_to_smiles(self, idx)
            smiles = smiles.replace('<bos>', '') # begin token
            smiles = smiles.replace('<eos>', '') # end token
            smiles = smiles.replace('<pad>', '') # pad token
            pred_smiles.append(smiles)

        # clear GPU memory
        if self.is_cuda_available:
            torch.cuda.empty_cache()
            gc.collect()

        return pred_smiles

    def __str__(self):
        return 'smi-ted-Large'
    

def load_smi_ted(folder="./smi_ted_large", 
              ckpt_filename="smi-ted-Large_30.pt",
              vocab_filename="bert_vocab_curated.txt"
              ):
    tokenizer = MolTranBertTokenizer(os.path.join(folder, vocab_filename))
    model = Smi_ted(tokenizer)
    model.load_checkpoint(os.path.join(folder, ckpt_filename))
    model.eval()
    print('Vocab size:', len(tokenizer.vocab))
    print(f'[INFERENCE MODE - {str(model)}]')
    return model