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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from pytorch_lightning import LightningDataModule
import torch_geometric
# from torch_geometric.loader import DataLoader
from torch.utils.data import DataLoader
from torch_geometric.loader.dataloader import Collater
from data_provider.reaction_action_dataset import ActionDataset
from data_provider.synthesis_dataset import SynthesisDataset
from data_provider.caption_dataset import CaptionDataset
from data_provider.chebi_dataset import ChEBI_dataset
import re
# we split individual characters inside special tokens like [START_DNA]
CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
# token added to implement a custom sequence tokenization. This token is added at
# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
# that they do not occur in the corpus. The digits are escaped so that the token does not appear
# literally in the source code in case we ever include it in the training data.
SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
def _insert_split_marker(m: re.Match):
"""
Applies split marker based on a regex match of special tokens such as
[START_DNA].
Parameters
----------
n : str
Input text to split
Returns
----------
str - the text with the split token added
"""
start_token, _, sequence, end_token = m.groups()
sequence = re.sub(r"(.)", fr"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
def smiles_handler(text, mol_ph, is_gal=True):
smiles_list = []
for match in CUSTOM_SEQ_RE.finditer(text):
smiles = match.group(3)
smiles_list.append(smiles)
if is_gal:
text = CUSTOM_SEQ_RE.sub(r'\1\3\4%s' % (mol_ph), text)
text = escape_custom_split_sequence(text)
return text, smiles_list
else:
text = CUSTOM_SEQ_RE.sub(r'\3%s' % (mol_ph), text)
return text, smiles_list
def escape_custom_split_sequence(text):
"""
Applies custom splitting to the text for GALILEO's tokenization
Parameters
----------
text : str
Input text to split
Returns
----------
str - the text with the split token added
"""
return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
class TrainCollater:
def __init__(self, tokenizer, text_max_len, rxn_max_len, mol_ph, mol_token_id, is_gal=True, use_graph=True, use_qa_pair=True):
self.rxn_max_len = rxn_max_len
self.text_max_len = text_max_len
self.tokenizer = tokenizer
self.collater = Collater([], [])
self.mol_ph = mol_ph
self.mol_token_id = mol_token_id
self.is_gal = is_gal
self.use_graph = use_graph
self.use_qa_pair = use_qa_pair
def __call__(self, batch):
return self.collate_qa(batch) if self.use_qa_pair else self.collate(batch)
def collate(self, batch):
rxn_ids, graphs, texts, smiles_prompt = zip(*batch)
if graphs:
graphs = self.collater(graphs)
## deal with prompt
if self.use_graph:
smiles_prompt = [smiles_handler(p, self.mol_ph, self.is_gal)[0] for p in smiles_prompt]
else:
smiles_prompt = [escape_custom_split_sequence(p) for p in smiles_prompt]
self.tokenizer.padding_side = 'left'
smiles_prompt_tokens = self.tokenizer(text=smiles_prompt,
truncation=False,
padding='longest',
add_special_tokens=True,
return_tensors='pt',
return_attention_mask=True)
is_mol_token = smiles_prompt_tokens.input_ids == self.mol_token_id
smiles_prompt_tokens['is_mol_token'] = is_mol_token
self.tokenizer.padding_side = 'right'
text_tokens = self.tokenizer(text=texts,
truncation=True,
padding='longest',
add_special_tokens=True,
max_length=self.text_max_len,
return_tensors='pt',
return_attention_mask=True)
return rxn_ids, graphs, smiles_prompt_tokens, text_tokens
def collate_qa(self, batch):
rxn_ids, graphs, texts, input_prompt = zip(*batch)
graphs = [graph for graph_batch in graphs for graph in graph_batch]
if graphs:
graphs = self.collater(graphs)
## deal with prompt
if self.use_graph:
input_prompt = [smiles_handler(p, self.mol_ph, self.is_gal)[0] for p in input_prompt]
else:
input_prompt = [escape_custom_split_sequence(p) for p in input_prompt]
self.tokenizer.padding_side = 'right'
qa_pair = [[q, a] for q, a in zip(input_prompt, texts)]
qa_batch = self.tokenizer(qa_pair,
truncation=True,
padding='longest',
add_special_tokens=True,
max_length=self.rxn_max_len + self.text_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=True)
is_mol_token = qa_batch.input_ids == self.mol_token_id
qa_batch['is_mol_token'] = is_mol_token
return rxn_ids, graphs, qa_batch
class InferenceCollater:
def __init__(self, tokenizer, text_max_len, rxn_max_len, mol_ph, mol_token_id, is_gal=True):
self.text_max_len = text_max_len
self.rxn_max_len = rxn_max_len
self.tokenizer = tokenizer
self.collater = Collater([], [])
self.mol_ph = mol_ph
self.mol_token_id = mol_token_id
self.is_gal = is_gal
def __call__(self, batch):
rxn_ids, graphs, texts, input_prompt = zip(*batch)
inputs = input_prompt
graphs = [graph for graph_batch in graphs for graph in graph_batch]
if graphs:
graphs = self.collater(graphs)
input_prompt = [smiles_handler(p, self.mol_ph, self.is_gal)[0] for p in input_prompt]
## deal with prompt
self.tokenizer.padding_side = 'left'
input_prompt_tokens = self.tokenizer(input_prompt,
truncation=True,
padding='longest',
add_special_tokens=True,
max_length=self.rxn_max_len,
return_tensors='pt',
return_attention_mask=True)
is_mol_token = input_prompt_tokens.input_ids == self.mol_token_id
input_prompt_tokens['is_mol_token'] = is_mol_token
return rxn_ids, graphs, input_prompt_tokens, texts, inputs
class TuneDM(LightningDataModule):
def __init__(
self,
num_workers: int = 0,
batch_size: int = 256,
root: str = 'data/',
text_max_len: int = 128,
smi_max_len: int = 128,
rxn_max_len: int = 128,
tokenizer=None,
downstream_task='action',
args=None,
):
super().__init__()
self.args = args
self.batch_size = batch_size
self.inference_batch_size = args.inference_batch_size
self.num_workers = num_workers
self.rxn_max_len = rxn_max_len
self.text_max_len = text_max_len
self.prompt = args.prompt
DownstreamDataset = {
'action': ActionDataset,
'synthesis': SynthesisDataset,
'caption': CaptionDataset,
'chebi': ChEBI_dataset,
}[downstream_task]
ds_args = {
'use_graph': not args.disable_graphs,
'disable_graph_cache': args.disable_graph_cache,
'smiles_type': args.smiles_type,
}
if downstream_task == 'action':
ds_args['predict_rxn_condition'] = args.predict_rxn_condition
if downstream_task == 'synthesis':
ds_args['roundrobin_train'] = args.roundrobin_train
ds_args['test_subset'] = args.test_subset
self.train_dataset = DownstreamDataset(root, 'train', smi_max_len, **ds_args)
self.val_dataset = DownstreamDataset(root, 'valid', smi_max_len, **ds_args)
self.test_dataset = DownstreamDataset(root, 'test', smi_max_len, **ds_args)
self.init_tokenizer(tokenizer)
self.mol_ph_token = '<mol>' * self.args.num_query_token
self.is_gal = args.opt_model.find('galactica') >= 0
self.use_graph = not args.disable_graphs
self.is_t5 = args.opt_model.find('t5') >= 0
def init_tokenizer(self, tokenizer):
self.tokenizer = tokenizer
self.train_dataset.tokenizer = tokenizer
self.val_dataset.tokenizer = tokenizer
self.test_dataset.tokenizer = tokenizer
self.mol_token_id = self.tokenizer.mol_token_id
# self.tokenizer.mol_token_id = tokenizer("<mol>", add_special_tokens=False).input_ids[0]
def train_dataloader(self):
if self.args.roundrobin_train:
self.train_dataset.reload_data()
if hasattr(self.train_dataset, 'renew_r_smiles'):
self.train_dataset.renew_r_smiles()
loader = DataLoader(
self.train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=False,
drop_last=True,
persistent_workers=True,
collate_fn=TrainCollater(
tokenizer=self.tokenizer,
text_max_len=self.text_max_len,
rxn_max_len=self.rxn_max_len,
mol_ph=self.mol_ph_token,
mol_token_id=self.mol_token_id,
is_gal=self.is_gal,
use_graph=self.use_graph,
use_qa_pair=not self.is_t5,
),
)
return loader
def val_dataloader(self):
test_loader = DataLoader(
self.test_dataset,
batch_size=self.inference_batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=False,
drop_last=False,
persistent_workers=True,
collate_fn=InferenceCollater(
tokenizer=self.tokenizer,
text_max_len=self.text_max_len,
rxn_max_len=self.rxn_max_len,
mol_ph=self.mol_ph_token,
mol_token_id=self.mol_token_id,
is_gal=self.is_gal
),
)
return [test_loader]
val_loader = DataLoader(
self.val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=False,
drop_last=False,
persistent_workers=True,
collate_fn=InferenceCollater(
tokenizer=self.tokenizer,
text_max_len=self.text_max_len,
rxn_max_len=self.rxn_max_len,
mol_ph=self.mol_ph_token,
mol_token_id=self.mol_token_id,
is_gal=self.is_gal
),
)
return [val_loader, test_loader]
def test_dataloader(self):
loader = DataLoader(
self.test_dataset,
batch_size=self.inference_batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=False,
drop_last=False,
persistent_workers=True,
collate_fn=InferenceCollater(
tokenizer=self.tokenizer,
text_max_len=self.text_max_len,
rxn_max_len=self.rxn_max_len,
mol_ph=self.mol_ph_token,
mol_token_id=self.mol_token_id,
is_gal=self.is_gal
),
)
return loader
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