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# Licensed under the MIT License.
import json
import random
import torch
import os
import numpy as np
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset
from data_provider.gal_helpers import escape_custom_split_sequence
from pathlib import Path
from torch.utils.data.dataloader import default_collate
class ProteinChatCollater(object):
def __init__(self, tokenizer, q_max_len, a_max_len, use_gal):
self.tokenizer = tokenizer
self.q_max_len = q_max_len
self.a_max_len = a_max_len
self.use_gal = use_gal
def __call__(self, batch):
embeds, prot_seqs, questions, answers, q_types = zip(*batch)
max_embed_len = 896
## concate
if False:
max_dim = max([e.shape[0] for e in embeds])
padded_embeds = []
for embed in embeds:
shape_dim0 = embed.shape[0]
pad1 = ((0, max_dim - shape_dim0), (0, 0), (0, 0))
padded_embeds.append(np.pad(embed, pad1, mode='constant'))
padded_embeds = default_collate(padded_embeds).squeeze(dim=2)[:,:1024,:]
else:
padded_embeds = torch.zeros(len(embeds), max_embed_len, 512)
for i in range(len(embeds)):
padded_embeds[i, :embeds[i].shape[0], :] = embeds[i][:max_embed_len, :]
padded_embeds = padded_embeds.detach()
assert len(prot_seqs) == len(questions) == len(answers)
if self.use_gal:
questions = [escape_custom_split_sequence(q) for q in questions]
answers = [a + '\n' for a in answers]
self.tokenizer.padding_side = 'left'
q_batch = self.tokenizer(questions,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.q_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
self.tokenizer.padding_side = 'right'
a_batch = self.tokenizer(answers,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.a_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
prot_mask = torch.ones(padded_embeds.shape[0], padded_embeds.shape[1], dtype=torch.bool)
return (padded_embeds, prot_mask), q_batch, a_batch
class InferenceCollater(object):
def __init__(self, tokenizer, q_max_len, a_max_len, use_gal):
self.tokenizer = tokenizer
self.q_max_len = q_max_len
self.a_max_len = a_max_len
self.use_gal = use_gal
def __call__(self, batch):
embeds, prot_seqs, questions, answers, q_types = zip(*batch)
max_embed_len = 896
## concate
if False:
max_dim = max([e.shape[0] for e in embeds])
padded_embeds = []
for embed in embeds:
shape_dim0 = embed.shape[0]
pad1 = ((0, max_dim - shape_dim0), (0, 0), (0, 0))
padded_embeds.append(np.pad(embed, pad1, mode='constant'))
padded_embeds = default_collate(padded_embeds).squeeze(dim=2)[:,:1024,:]
else:
padded_embeds = torch.zeros(len(embeds), max_embed_len, 512)
for i in range(len(embeds)):
padded_embeds[i, :embeds[i].shape[0], :] = embeds[i][:max_embed_len, :]
padded_embeds = padded_embeds.detach()
assert len(prot_seqs) == len(questions) == len(answers)
if self.use_gal:
questions = [escape_custom_split_sequence(q) for q in questions]
answers = [a + '\n' for a in answers]
self.tokenizer.padding_side = 'left'
q_batch = self.tokenizer(questions,
truncation=True,
padding='max_length',
add_special_tokens=True,
max_length=self.q_max_len,
return_tensors='pt',
return_attention_mask=True,
return_token_type_ids=False)
prot_mask = torch.ones(padded_embeds.shape[0], padded_embeds.shape[1], dtype=torch.bool)
target_dict = {'answers': answers, "q_types": q_types}
return (padded_embeds, prot_mask), q_batch, target_dict
class ProteinChatDM(LightningDataModule):
def __init__(
self,
root: str = 'data/',
args=None,
):
super().__init__()
self.args = args
self.batch_size = args.batch_size
self.inference_batch_size = args.inference_batch_size
self.num_workers = args.num_workers
self.q_max_len = args.q_max_len
self.a_max_len = args.a_max_len
self.prompt = args.prompt
self.train_dataset = ProteinChatDataset(root, 'train.txt', prompt="### Human: {}\n### Assistant: ", pt_file_path=args.pt_file_path)
self.val_dataset = ProteinChatDataset(root, 'val.txt', prompt="### Human: {}\n### Assistant: ", pt_file_path=args.pt_file_path)
self.test_dataset = ProteinChatDataset(root, 'test.txt', prompt="### Human: {}\n### Assistant: ", pt_file_path=args.pt_file_path)
self.tokenizer = None
self.use_gal = args.llm_name.find('gal') >= 0
def init_tokenizer(self, tokenizer):
self.tokenizer = tokenizer
def train_dataloader(self):
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=False,
collate_fn=ProteinChatCollater(self.tokenizer, self.q_max_len, self.a_max_len, self.use_gal),
)
return loader
def val_dataloader(self):
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=False,
collate_fn=ProteinChatCollater(self.tokenizer, self.q_max_len, self.a_max_len, self.use_gal),
)
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=False,
collate_fn=InferenceCollater(self.tokenizer, self.q_max_len, self.a_max_len, self.use_gal),
)
return [val_loader, test_loader]
def add_model_specific_args(parent_parser):
parser = parent_parser.add_argument_group("Data module")
parser.add_argument('--num_workers', type=int, default=2)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--inference_batch_size', type=int, default=4)
parser.add_argument('--root', type=str, default='data/SwissProtV3')
parser.add_argument('--q_max_len', type=int, default=30)
parser.add_argument('--a_max_len', type=int, default=36)
parser.add_argument('--prompt', type=str, default='[START_AMINO]{}[END_AMINO]. Question: {} Answer:')
parser.add_argument('--pt_file_path', type=str, default='/home/XXXX-2/proteinchatdata/proteinchat')
return parent_parser
class ProteinChatDataset(Dataset):
def __init__(self, root_path, subset, pt_file_path, prompt="Question: {} Answer:"):
super(ProteinChatDataset, self).__init__()
self.data_path = Path(root_path) / subset
self.qa_path = Path(root_path) / 'qa_all.json'
self.q_type_path = Path(root_path) / 'q_types.txt'
self.prompt = prompt
## load dataset
with open(self.qa_path, 'r') as f:
qa_data = json.load(f)
with open(self.data_path, 'r') as f:
lines = f.readlines()
pdb2seq = [line.strip().split('\t') for line in lines]
## process dataset
pdb_set = set(i[0] for i in pdb2seq)
## filter qa data
qa_data = {k: v for k, v in qa_data.items() if k in pdb_set}
assert len(qa_data) == len(pdb_set), print(len(qa_data), len(pdb_set))
pt_file = Path(pt_file_path).glob('*.pt')
pt_file_ids = {f.name.split('.pt')[0] for f in pt_file}
self.pt_file_path = pt_file_path
## load q types
with open(self.q_type_path, 'r') as f:
q_types = [line.strip().split('\t') for line in f.readlines()]
self.q_type_dict = {q: t for q, t in q_types}
## generate qa data
self.data_list = []
for pdb_id, seq in pdb2seq:
if pdb_id not in pt_file_ids:
continue
qa_list = qa_data[pdb_id]
for qa in qa_list:
q = qa['Q']
a = str(qa['A'])
self.data_list.append((pdb_id, seq, q, a))
def shuffle(self):
random.shuffle(self.data_list)
return self
def __len__(self):
return len(self.data_list)
def __getitem__(self, index):
pdb_id, seq, q, a = self.data_list[index]
q_type = self.q_type_dict[q]
path = os.path.join(self.pt_file_path, pdb_id + '.pt')
embed = torch.load(path, map_location=torch.device('cpu'))
embed = embed.squeeze(dim=1)
embed = embed.detach()
q = self.prompt.format(q)
return embed, seq, q, a, q_type
if __name__ == '__main__':
dataset = ProteinChatDataset('./data/PDBDataset', 'train.txt')
dataset.shuffle()
for i in range(1000):
print(dataset[i][0].shape)
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