# import packages
import sys
sys.path.append("C:/Users/Andrew/Desktop/Projects/Deep Learning/utils")
from tools import AverageMeter, ProgressBar
from radam import RAdam
import torch
from torch.utils.data import Dataset, random_split, DataLoader, RandomSampler
import torch.nn.functional as F
import numpy as np
import pandas as pd
from transformers import DistilBertModel, DistilBertTokenizer
from transformers import get_linear_schedule_with_warmup, AdamW
from torch.cuda.amp import autocast, GradScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
import time, datetime
SEED = 15
torch.manual_seed(SEED)## <torch._C.Generator object at 0x000000001FC6D3B0>## <torch.cuda.amp.autocast_mode.autocast object at 0x000000001E95B848>Natural language processing and computer vision methods generate high-dimensional vectors that represent text and images, yet traditional databases that can be queried like SQL are not adapted to these new representations. Given enough text and media, this information can quickly encompass billions of vectors. To find similar entries means that we must find similar high-dimensional vectors which is inefficient and likely impossible with standard query languages. Similarity search fills this void by searching for similar vectors; those nearby in Euclidean space. We can leverage similarity search algorithms once our vectors are generated by deep learning algorithms. In this post, we will use Faiss – Facebook AI Similarity Search.
First, we prepare our torch data set and instantiate our tokenizer.
# set tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
# Create Dataset
class CSVDataset(Dataset):
"""Propganda dataset."""
def __init__(self, csv_file, transform=None):
"""
Args:
csv_file (string): Path to the csv file with annotations.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
# initialize
self.data_frame = pd.read_csv(csv_file)
# text col
self.text_features = self.data_frame['body']
# target col
self.target = self.data_frame['target']
# initialize the transform if specified
self.transform = transform
# encode outcome
self.data_frame['target'] = LabelEncoder().fit_transform(self.data_frame['target'])
# get length of df
def __len__(self):
return len(self.data_frame)
# get target
def __get_target__(self):
return self.data_frame.target
# get df filtered by indices
def __get_values__(self, indices):
return self.data_frame.iloc[indices]
# pull a sample of data
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
# hold sample in a dict
sample = {'features': self.text_features.iloc[idx],
'target': self.target.iloc[idx],
'idx': idx}
if self.transform:
sample = self.transform(sample)
return sample
class Tokenize_Transform():
# retrieve sample and unpack it
def __call__(self, sample):
text, target, idx = (sample['features'],
sample['target'],
sample['idx'])
# transform text to input ids and attn masks
tokenizer_output = tokenizer.encode_plus(
text, # document to encode.
add_special_tokens=True, # add '[CLS]' and '[SEP]'
max_length=512, # set max length
truncation=True, # truncate longer messages
pad_to_max_length=True, # add padding
return_attention_mask=True, # create attn. masks
return_tensors='pt' # return pytorch tensors
)
input_ids, attn_mask = tokenizer_output['input_ids'], tokenizer_output['attention_mask']
# yield another dict
return {'features': torch.as_tensor(input_ids,
dtype=torch.long,
device=device).squeeze(1),
'attn_mask': torch.as_tensor(attn_mask,
dtype=torch.long,
device=device).squeeze(1),
'target': torch.as_tensor(target,
dtype=torch.long,
device=device),
'idx': torch.as_tensor(idx,
dtype=torch.int,
device=device)}Next we create our data sets and split them into train, validation, and test sets.
# instantiate the lazy data set
csv_dataset = CSVDataset(csv_file='C:\\Users\\Andrew\\Desktop\\test_export.csv', transform=Tokenize_Transform())
# check data
for i, batch in enumerate(csv_dataset):
if i == 0:
break
# set train, valid, and test size
train_size = int(0.8 * len(csv_dataset))
valid_size = int(0.1 * len(csv_dataset))
# use random split to create three data sets; +1 for odd number of data
train_ds, valid_ds, test_ds = torch.utils.data.random_split(csv_dataset, [train_size, valid_size, valid_size+1])Now we create our DistilBERT model.
# create distilbert
class DistillBERT(torch.nn.Module):
def __init__(self):
super(DistillBERT, self).__init__()
# load model
self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased", output_hidden_states=True)
# pre-classifier layer
self.pre_classifier = torch.nn.Linear(768, 768)
# drop out
self.dropout = torch.nn.Dropout(0.3)
# final classification layer
self.classifier = torch.nn.Linear(768, 2) # [features, targets]
def forward(self, input_ids, attention_mask):
# generate outputs from BERT
output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
embeddings = output_1[1] # 2nd tuple is the embeddings
# stack
embeddings = torch.stack(embeddings) # [layers, batch_sz, tokens, features]]
# grab second to last layer
embeddings = embeddings[-2] # [batch_sz, tokens, features]]
# just get the CLS token
embeddings = embeddings[:, 0, :] # [batch_sz, features]]
# send through pre-classifying linear layer
pooled_output = self.pre_classifier(embeddings)
# relu
pooled_output = torch.nn.ReLU()(pooled_output)
# add dropout
pooled_output = self.dropout(pooled_output)
# final classifying layer to yield logits
logits = self.classifier(pooled_output)
return logits
# load the model
model = DistillBERT().to(device)A weighted sampler is prepared to handle class imbalance:
# prepare weighted sampling for imbalanced classification
def create_sampler(train_ds, csv_dataset):
# get indicies from train split
train_indices = train_ds.indices
# generate class distributions [y1, y2, etc...]
bin_count = np.bincount(csv_dataset.__get_target__()[train_indices])
# weight gen
weight = 1. / bin_count.astype(np.float32)
# produce weights for each observation in the data set
samples_weight = torch.tensor([weight[t] for t in csv_dataset.__get_target__()[train_indices]])
# prepare sampler
sampler = torch.utils.data.WeightedRandomSampler(weights=samples_weight,
num_samples=len(samples_weight),
replacement=True)
return samplerAnd data loaders are prepared:
# create sampler for the training ds
train_sampler = create_sampler(train_ds, csv_dataset)
# create DataLoaders
train_dataloader = DataLoader(train_ds,
batch_size=16,
sampler=train_sampler,
shuffle=False)
# no_grad means we can train more quickly, hence batch_size*2
valid_dataloader = DataLoader(valid_ds,
batch_size=16*2,
shuffle=True)
test_dataloader = DataLoader(test_ds,
batch_size=16*2,
shuffle=True)We can check the integrity of our data sampler like so:
# check sampler's integrity
for i, batch in enumerate(train_dataloader):
print("batch index {}, 0/1: {}/{}".format(
i, (batch['target'] == 0).sum(), (batch['target'] == 1).sum()))
if i == 10:
break## batch index 0, 0/1: 12/4
## batch index 1, 0/1: 10/6
## batch index 2, 0/1: 8/8
## batch index 3, 0/1: 7/9
## batch index 4, 0/1: 7/9
## batch index 5, 0/1: 11/5
## batch index 6, 0/1: 9/7
## batch index 7, 0/1: 10/6
## batch index 8, 0/1: 6/10
## batch index 9, 0/1: 7/9
## batch index 10, 0/1: 7/9Here we use mixed precision, create our optimizer and learning rate scheduler, and set the epochs.
# create gradient scaler for mixed precision
scaler = GradScaler()
# create optimizer -- add L2 regularization
optimizer = RAdam(model.parameters(), lr=2e-6, weight_decay=0.5)
# set epochs
epochs = 4
# set LR scheduler
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer,
max_lr=2e-5,
total_steps=len(train_dataloader)*epochs)Our usual training and validation functions:
# train function
def train(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Training')
train_loss = AverageMeter()
model.train()
for batch_idx, batch in enumerate(dataloader):
optimizer.zero_grad()
with autocast():
logits = model(input_ids=batch['features'].squeeze(1), attention_mask=batch['attn_mask'].squeeze(1))
loss = F.cross_entropy(logits, batch['target'])
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
pbar(step=batch_idx, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
return {'loss': train_loss.avg}
# valid/test function
def test(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Testing')
valid_loss = AverageMeter()
valid_acc = AverageMeter()
valid_f1 = AverageMeter()
model.eval()
count = 0
with torch.no_grad():
for batch_idx, batch in enumerate(dataloader):
logits = model(input_ids=batch['features'].squeeze(1), attention_mask=batch['attn_mask'].squeeze(1))
loss = F.cross_entropy(logits, batch['target'])
pred = logits.argmax(dim=1, keepdim=True)
correct = pred.eq(batch['target'].view_as(pred)).sum().item()
f1 = f1_score(pred.to("cpu").numpy(), batch['target'].to("cpu").numpy(), average='macro')
valid_f1.update(f1, n=batch['features'].size(0))
valid_loss.update(loss, n=batch['features'].size(0))
valid_acc.update(correct, n=1)
count += batch['features'].size(0)
pbar(step=batch_idx)
return {'valid_loss': valid_loss.avg,
'valid_acc': valid_acc.sum /count,
'valid_f1': valid_f1.avg}# training
best_loss = 0
for epoch in range(1, epochs + 1):
train_log = train(train_dataloader)
valid_log = test(valid_dataloader)
logs = dict(train_log, **valid_log)
for key, value in logs.items():
if key == 'valid_loss':
if value.item() < best_loss:
torch.save(model.state_dict(), 'distilbert-model1.pt') # torch save
best_loss = value.item()
show_info = f'\nEpoch: {epoch} - ' + "-".join([f' {key}: {value:.4f} ' for key, value in logs.items()])
print(show_info)##
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[Training] 503/503 [==============================] 301.4ms/step loss: 0.1542
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[Testing] 32/32 [==============================] 366.7ms/step
## Epoch: 1 - loss: 0.5118 - valid_loss: 0.3012 - valid_acc: 0.8665 - valid_f1: 0.8171
##
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[Training] 503/503 [==============================] 289.4ms/step loss: 0.0448
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[Testing] 32/32 [==============================] 378.0ms/step
## Epoch: 2 - loss: 0.2766 - valid_loss: 0.3644 - valid_acc: 0.8297 - valid_f1: 0.7916
##
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[Training] 503/503 [==============================] 298.0ms/step loss: 0.0575
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[Testing] 32/32 [==============================] 359.1ms/step
## Epoch: 3 - loss: 0.2242 - valid_loss: 0.2920 - valid_acc: 0.8805 - valid_f1: 0.8397
##
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## Epoch: 4 - loss: 0.1755 - valid_loss: 0.2870 - valid_acc: 0.8865 - valid_f1: 0.8476
##
## C:/Users/Andrew/Desktop/Projects/Deep Learning/utils\radam.py:60: UserWarning: This overload of add_ is deprecated:
## add_(Number alpha, Tensor other)
## Consider using one of the following signatures instead:
## add_(Tensor other, *, Number alpha) (Triggered internally at ..\torch\csrc\utils\python_arg_parser.cpp:766.)
## exp_avg.mul_(beta1).add_(1 - beta1, grad)##
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[Testing] 32/32 [==============================] 318.9ms/step## {'valid_loss': tensor(0.3638, device='cuda:0'), 'valid_acc': 0.8606965174129353, 'valid_f1': 0.8001156244602546}Here we prepare a new data loader and use the entire data set.
# export embeddings for semantic similary search
# create dataloader for whole data set -- batch size of 1 is fastest
train_dataloader = DataLoader(csv_dataset,
batch_size=1,
shuffle=False)Next, we instantiate our model and tweak it slightly to only retrieve the embeddings from the CLS token.
# tweak the model
class DistillBERT_Emb(torch.nn.Module):
def __init__(self):
super(DistillBERT_Emb, self).__init__()
# load model
self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased", output_hidden_states=True)
# pre-classifier layer
self.pre_classifier = torch.nn.Linear(768, 768)
# drop out
self.dropout = torch.nn.Dropout(0.3)
# final classification layer
self.classifier = torch.nn.Linear(768, 2) # [features, targets]
def forward(self, input_ids, attention_mask):
# generate outputs from BERT
with torch.no_grad():
output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
embeddings = output_1[1] # 2nd tuple is the embeddings
# stack
embeddings = torch.stack(embeddings) # [layers, batch_sz, tokens, features]]
# grab second to last layer
embeddings = embeddings[-2] # [batch_sz, tokens, features]]
# just get the CLS token
embeddings = embeddings[:, 0, :] # [batch_sz, features]]
return embeddingsNow we instantiate our tweaked model and feed it fine-tuned weights.
# instantiate the model and load the weights
model = DistillBERT_Emb().to(device)
model.load_state_dict(torch.load('distilbert-model1.pt'))## <All keys matched successfully>In this section, we lazily load our data in batches of 1 and generate and save our embeddings. It is important that we detach the embeddings from the GPU to prevent the GPU’s memory from exceeding capacity and crashing our training.
# start time
t0 = time.time()
batch_num = 0
num_documents = len(csv_dataset)
# time function
def format_time(elapsed):
'''
Takes a time in seconds and returns a string hh:mm:ss
'''
# round to the nearest second.
elapsed_rounded = int(round((elapsed)))
# format as hh:mm:ss
return str(datetime.timedelta(seconds=elapsed_rounded))
# store embeddings
embeddings_container = []
print('Generating embeddings for all {:,} documents...'.format(len(csv_dataset)))## Generating embeddings for all 10,045 documents...for step, batch in enumerate(train_dataloader):
if step % 20 == 0 and not batch_num == 0:
elapsed = format_time(time.time() - t0)
# calc time remaining
rows_per_sec = (time.time() - t0) / batch_num
remaining_sec = rows_per_sec * (num_documents - batch_num)
remaining = format_time(remaining_sec)
# report progress
print('Documents {:>7,} of {:>7,}. Elapsed: {:}. Remaining: {:}'.format(batch_num, num_documents, elapsed, remaining))
# get embedding
embeddings = model(input_ids=batch['features'].squeeze(1), attention_mask=batch['attn_mask'].squeeze(1))
# send to cpu or GPU will run out of memory
embeddings_container.append(embeddings.to('cpu').numpy())
batch_num +=1
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## Documents 10,040 of 10,045. Elapsed: 0:02:19. Remaining: 0:00:00Next we reshape and save our data as a numpy file for use in Faiss.
## (10045, 1, 768)To review the remainder of this similarity search exercise, please view this notebook.