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train_bert_hf.py
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
import argparse
from pathlib import Path
import time
import pandas as pd
import torch
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
class IMDBDataset(Dataset):
def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256, use_attention_mask=False):
self.data = pd.read_csv(csv_file)
self.max_length = max_length if max_length is not None else self._longest_encoded_length(tokenizer)
self.pad_token_id = pad_token_id
self.use_attention_mask = use_attention_mask
# Pre-tokenize texts and create attention masks if required
self.encoded_texts = [
tokenizer.encode(text, truncation=True, max_length=self.max_length)
for text in self.data["text"]
]
self.encoded_texts = [
et + [pad_token_id] * (self.max_length - len(et))
for et in self.encoded_texts
]
if self.use_attention_mask:
self.attention_masks = [
self._create_attention_mask(et)
for et in self.encoded_texts
]
else:
self.attention_masks = None
def _create_attention_mask(self, encoded_text):
return [1 if token_id != self.pad_token_id else 0 for token_id in encoded_text]
def __getitem__(self, index):
encoded = self.encoded_texts[index]
label = self.data.iloc[index]["label"]
if self.use_attention_mask:
attention_mask = self.attention_masks[index]
else:
attention_mask = torch.ones(self.max_length, dtype=torch.long)
return (
torch.tensor(encoded, dtype=torch.long),
torch.tensor(attention_mask, dtype=torch.long),
torch.tensor(label, dtype=torch.long)
)
def __len__(self):
return len(self.data)
def _longest_encoded_length(self, tokenizer):
max_length = 0
for text in self.data["text"]:
encoded_length = len(tokenizer.encode(text))
if encoded_length > max_length:
max_length = encoded_length
return max_length
def calc_loss_batch(input_batch, attention_mask_batch, target_batch, model, device):
attention_mask_batch = attention_mask_batch.to(device)
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
# logits = model(input_batch)[:, -1, :] # Logits of last output token
logits = model(input_batch, attention_mask=attention_mask_batch).logits
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss
# Same as in chapter 5
def calc_loss_loader(data_loader, model, device, num_batches=None):
total_loss = 0.
if num_batches is None:
num_batches = len(data_loader)
else:
# Reduce the number of batches to match the total number of batches in the data loader
# if num_batches exceeds the number of batches in the data loader
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, attention_mask_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
loss = calc_loss_batch(input_batch, attention_mask_batch, target_batch, model, device)
total_loss += loss.item()
else:
break
return total_loss / num_batches
@torch.no_grad() # Disable gradient tracking for efficiency
def calc_accuracy_loader(data_loader, model, device, num_batches=None):
model.eval()
correct_predictions, num_examples = 0, 0
if num_batches is None:
num_batches = len(data_loader)
else:
num_batches = min(num_batches, len(data_loader))
for i, (input_batch, attention_mask_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
attention_mask_batch = attention_mask_batch.to(device)
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
# logits = model(input_batch)[:, -1, :] # Logits of last output token
logits = model(input_batch, attention_mask=attention_mask_batch).logits
predicted_labels = torch.argmax(logits, dim=1)
num_examples += predicted_labels.shape[0]
correct_predictions += (predicted_labels == target_batch).sum().item()
else:
break
return correct_predictions / num_examples
def evaluate_model(model, train_loader, val_loader, device, eval_iter):
model.eval()
with torch.no_grad():
train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
model.train()
return train_loss, val_loss
def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
eval_freq, eval_iter, max_steps=None):
# Initialize lists to track losses and tokens seen
train_losses, val_losses, train_accs, val_accs = [], [], [], []
examples_seen, global_step = 0, -1
# Main training loop
for epoch in range(num_epochs):
model.train() # Set model to training mode
for input_batch, attention_mask_batch, target_batch in train_loader:
optimizer.zero_grad() # Reset loss gradients from previous batch iteration
loss = calc_loss_batch(input_batch, attention_mask_batch, target_batch, model, device)
loss.backward() # Calculate loss gradients
optimizer.step() # Update model weights using loss gradients
examples_seen += input_batch.shape[0] # New: track examples instead of tokens
global_step += 1
# Optional evaluation step
if global_step % eval_freq == 0:
train_loss, val_loss = evaluate_model(
model, train_loader, val_loader, device, eval_iter)
train_losses.append(train_loss)
val_losses.append(val_loss)
print(f"Ep {epoch+1} (Step {global_step:06d}): "
f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
if max_steps is not None and global_step > max_steps:
break
# New: Calculate accuracy after each epoch
train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
train_accs.append(train_accuracy)
val_accs.append(val_accuracy)
if max_steps is not None and global_step > max_steps:
break
return train_losses, val_losses, train_accs, val_accs, examples_seen
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--trainable_layers",
type=str,
default="all",
help=(
"Which layers to train. Options: 'all', 'last_block', 'last_layer'."
)
)
parser.add_argument(
"--use_attention_mask",
type=str,
default="true",
help=(
"Whether to use a attention mask for padding tokens. Options: 'true', 'false'."
)
)
parser.add_argument(
"--model",
type=str,
default="distilbert",
help=(
"Which model to train. Options: 'distilbert', 'bert', 'roberta'."
)
)
parser.add_argument(
"--num_epochs",
type=int,
default=1,
help=(
"Number of epochs."
)
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help=(
"Learning rate."
)
)
args = parser.parse_args()
###############################
# Load model
###############################
torch.manual_seed(123)
if args.model == "distilbert":
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased", num_labels=2
)
model.out_head = torch.nn.Linear(in_features=768, out_features=2)
for param in model.parameters():
param.requires_grad = False
if args.trainable_layers == "last_layer":
for param in model.out_head.parameters():
param.requires_grad = True
elif args.trainable_layers == "last_block":
for param in model.pre_classifier.parameters():
param.requires_grad = True
for param in model.distilbert.transformer.layer[-1].parameters():
param.requires_grad = True
elif args.trainable_layers == "all":
for param in model.parameters():
param.requires_grad = True
else:
raise ValueError("Invalid --trainable_layers argument.")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
elif args.model == "bert":
model = AutoModelForSequenceClassification.from_pretrained(
"bert-base-uncased", num_labels=2
)
model.classifier = torch.nn.Linear(in_features=768, out_features=2)
for param in model.parameters():
param.requires_grad = False
if args.trainable_layers == "last_layer":
for param in model.classifier.parameters():
param.requires_grad = True
elif args.trainable_layers == "last_block":
for param in model.classifier.parameters():
param.requires_grad = True
for param in model.bert.pooler.dense.parameters():
param.requires_grad = True
for param in model.bert.encoder.layer[-1].parameters():
param.requires_grad = True
elif args.trainable_layers == "all":
for param in model.parameters():
param.requires_grad = True
else:
raise ValueError("Invalid --trainable_layers argument.")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
elif args.model == "roberta":
model = AutoModelForSequenceClassification.from_pretrained(
"FacebookAI/roberta-large", num_labels=2
)
model.classifier.out_proj = torch.nn.Linear(in_features=1024, out_features=2)
for param in model.parameters():
param.requires_grad = False
if args.trainable_layers == "last_layer":
for param in model.classifier.parameters():
param.requires_grad = True
elif args.trainable_layers == "last_block":
for param in model.classifier.parameters():
param.requires_grad = True
for param in model.roberta.encoder.layer[-1].parameters():
param.requires_grad = True
elif args.trainable_layers == "all":
for param in model.parameters():
param.requires_grad = True
else:
raise ValueError("Invalid --trainable_layers argument.")
tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large")
else:
raise ValueError("Selected --model {args.model} not supported.")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
###############################
# Instantiate dataloaders
###############################
base_path = Path(".")
if args.use_attention_mask.lower() == "true":
use_attention_mask = True
elif args.use_attention_mask.lower() == "false":
use_attention_mask = False
else:
raise ValueError("Invalid argument for `use_attention_mask`.")
train_dataset = IMDBDataset(
base_path / "train.csv",
max_length=256,
tokenizer=tokenizer,
pad_token_id=tokenizer.pad_token_id,
use_attention_mask=use_attention_mask
)
val_dataset = IMDBDataset(
base_path / "validation.csv",
max_length=256,
tokenizer=tokenizer,
pad_token_id=tokenizer.pad_token_id,
use_attention_mask=use_attention_mask
)
test_dataset = IMDBDataset(
base_path / "test.csv",
max_length=256,
tokenizer=tokenizer,
pad_token_id=tokenizer.pad_token_id,
use_attention_mask=use_attention_mask
)
num_workers = 0
batch_size = 8
train_loader = DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True,
)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=False,
)
test_loader = DataLoader(
dataset=test_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=False,
)
###############################
# Train model
###############################
start_time = time.time()
torch.manual_seed(123)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=0.1)
train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
model, train_loader, val_loader, optimizer, device,
num_epochs=args.num_epochs, eval_freq=50, eval_iter=20,
max_steps=None
)
end_time = time.time()
execution_time_minutes = (end_time - start_time) / 60
print(f"Training completed in {execution_time_minutes:.2f} minutes.")
###############################
# Evaluate model
###############################
print("\nEvaluating on the full datasets ...\n")
train_accuracy = calc_accuracy_loader(train_loader, model, device)
val_accuracy = calc_accuracy_loader(val_loader, model, device)
test_accuracy = calc_accuracy_loader(test_loader, model, device)
print(f"Training accuracy: {train_accuracy*100:.2f}%")
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
print(f"Test accuracy: {test_accuracy*100:.2f}%")