import collections
import dataclasses
import functools
import torch
import torch.nn as nn
import torch.optim as optim
import tqdm
from datasets import load_dataset
from tokenizers import Tokenizer
from torch import Tensor
# BERT config and model defined previously
@dataclasses.dataclass
class BertConfig:
“”“Configuration for BERT model.”“”
vocab_size: int = 30522
num_layers: int = 12
hidden_size: int = 768
num_heads: int = 12
dropout_prob: float = 0.1
pad_id: int = 0
max_seq_len: int = 512
num_types: int = 2
class BertBlock(nn.Module):
“”“One transformer block in BERT.”“”
def __init__(self, hidden_size: int, num_heads: int, dropout_prob: float):
super().__init__()
self.attention = nn.MultiheadAttention(hidden_size, num_heads,
dropout=dropout_prob, batch_first=True)
self.attn_norm = nn.LayerNorm(hidden_size)
self.ff_norm = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout_prob)
self.feed_forward = nn.Sequential(
nn.Linear(hidden_size, 4 * hidden_size),
nn.GELU(),
nn.Linear(4 * hidden_size, hidden_size),
)
def forward(self, x: Tensor, pad_mask: Tensor) -> Tensor:
# self-attention with padding mask and post-norm
attn_output, _ = self.attention(x, x, x, key_padding_mask=pad_mask)
x = self.attn_norm(x + attn_output)
# feed-forward with GeLU activation and post-norm
ff_output = self.feed_forward(x)
x = self.ff_norm(x + self.dropout(ff_output))
return x
class BertPooler(nn.Module):
“”“Pooler layer for BERT to process the [CLS] token output.”“”
def __init__(self, hidden_size: int):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation = nn.Tanh()
def forward(self, x: Tensor) -> Tensor:
x = self.dense(x)
x = self.activation(x)
return x
class BertModel(nn.Module):
“”“Backbone of BERT model.”“”
def __init__(self, config: BertConfig):
super().__init__()
# embedding layers
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size,
padding_idx=config.pad_id)
self.type_embeddings = nn.Embedding(config.num_types, config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_seq_len, config.hidden_size)
self.embeddings_norm = nn.LayerNorm(config.hidden_size)
self.embeddings_dropout = nn.Dropout(config.dropout_prob)
# transformer blocks
self.blocks = nn.ModuleList([
BertBlock(config.hidden_size, config.num_heads, config.dropout_prob)
for _ in range(config.num_layers)
])
# [CLS] pooler layer
self.pooler = BertPooler(config.hidden_size)
def forward(self, input_ids: Tensor, token_type_ids: Tensor, pad_id: int = 0,
) -> tuple[Tensor, Tensor]:
# create attention mask for padding tokens
pad_mask = input_ids == pad_id
# convert integer tokens to embedding vectors
batch_size, seq_len = input_ids.shape
position_ids = torch.arange(seq_len, device=input_ids.device).unsqueeze(0)
position_embeddings = self.position_embeddings(position_ids)
type_embeddings = self.type_embeddings(token_type_ids)
token_embeddings = self.word_embeddings(input_ids)
x = token_embeddings + type_embeddings + position_embeddings
x = self.embeddings_norm(x)
x = self.embeddings_dropout(x)
# process the sequence with transformer blocks
for block in self.blocks:
x = block(x, pad_mask)
# pool the hidden state of the `[CLS]` token
pooled_output = self.pooler(x[:, 0, :])
return x, pooled_output
# Define new BERT model for question answering
class BertForQuestionAnswering(nn.Module):
“”“BERT model for SQuAD question answering.”“”
def __init__(self, config: BertConfig):
super().__init__()
self.bert = BertModel(config)
# Two outputs: start and end position logits
self.qa_outputs = nn.Linear(config.hidden_size, 2)
def forward(self,
input_ids: Tensor,
token_type_ids: Tensor,
pad_id: int = 0,
) -> tuple[Tensor, Tensor]:
# Get sequence output from BERT (batch_size, seq_len, hidden_size)
seq_output, pooled_output = self.bert(input_ids, token_type_ids, pad_id=pad_id)
# Project to start and end logits
logits = self.qa_outputs(seq_output)# (batch_size, seq_len, 2)
start_logits = logits[:, :, 0]# (batch_size, seq_len)
end_logits = logits[:, :, 1]# (batch_size, seq_len)
return start_logits, end_logits
# Load SQuAD dataset for question answering
dataset = load_dataset(“squad”)
# Load the pretrained BERT tokenizer
TOKENIZER_PATH = “wikitext-2_wordpiece.json”
tokenizer = Tokenizer.from_file(TOKENIZER_PATH)
# Setup collate function to tokenize question-context pairs for the model
def collate(batch: list[dict], tokenizer: Tokenizer, max_len: int,
) -> tuple[Tensor, Tensor, Tensor, Tensor]:
“”“Collate question-context pairs for the model.”“”
cls_id = tokenizer.token_to_id(“[CLS]”)
sep_id = tokenizer.token_to_id(“[SEP]”)
pad_id = tokenizer.token_to_id(“[PAD]”)
input_ids_list = []
token_type_ids_list = []
start_positions = []
end_positions = []
for item in batch:
# Tokenize question and context
question, context = item[“question”], item[“context”]
question_ids = tokenizer.encode(question).ids
context_ids = tokenizer.encode(context).ids
# Build input: [CLS] question [SEP] context [SEP]
input_ids = [cls_id, *question_ids, sep_id, *context_ids, sep_id]
token_type_ids = [0] * (len(question_ids)+2) + [1] * (len(context_ids)+1)
# Truncate or pad to max length
if len(input_ids) > max_len:
input_ids = input_ids[:max_len]
token_type_ids = token_type_ids[:max_len]
else:
input_ids.extend([pad_id] * (max_len – len(input_ids)))
token_type_ids.extend([1] * (max_len – len(token_type_ids)))
# Find answer position in tokens: Answer may not be in the context
start_pos = end_pos = 0
if len(item[“answers”][“text”]) > 0:
answers = tokenizer.encode(item[“answers”][“text”][0]).ids
# find the context offset of the answer in context_ids
for i in range(len(context_ids) – len(answers) + 1):
if context_ids[i:i+len(answers)] == answers:
start_pos = i + len(question_ids) + 2
end_pos = start_pos + len(answers) – 1
break
if end_pos >= max_len:
start_pos = end_pos = 0# answer is clipped, hence no answer
input_ids_list.append(input_ids)
token_type_ids_list.append(token_type_ids)
start_positions.append(start_pos)
end_positions.append(end_pos)
input_ids_list = torch.tensor(input_ids_list)
token_type_ids_list = torch.tensor(token_type_ids_list)
start_positions = torch.tensor(start_positions)
end_positions = torch.tensor(end_positions)
return (input_ids_list, token_type_ids_list, start_positions, end_positions)
batch_size = 16
max_len = 384# Longer for Q&A to accommodate context
collate_fn = functools.partial(collate, tokenizer=tokenizer, max_len=max_len)
train_loader = torch.utils.data.DataLoader(dataset[“train”], batch_size=batch_size,
shuffle=True, collate_fn=collate_fn)
val_loader = torch.utils.data.DataLoader(dataset[“validation”], batch_size=batch_size,
shuffle=False, collate_fn=collate_fn)
# Create Q&A model with a pretrained foundation BERT model
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
config = BertConfig()
model = BertForQuestionAnswering(config)
model.to(device)
model.bert.load_state_dict(torch.load(“bert_model.pth”, map_location=device))
# Training setup
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), lr=2e–5)
num_epochs = 3
for epoch in range(num_epochs):
model.train()
# Training
with tqdm.tqdm(train_loader, desc=f“Epoch {epoch+1}/{num_epochs}”) as pbar:
for batch in pbar:
# get batched data
input_ids, token_type_ids, start_positions, end_positions = batch
input_ids = input_ids.to(device)
token_type_ids = token_type_ids.to(device)
start_positions = start_positions.to(device)
end_positions = end_positions.to(device)
# forward pass
start_logits, end_logits = model(input_ids, token_type_ids)
# backward pass
optimizer.zero_grad()
start_loss = loss_fn(start_logits, start_positions)
end_loss = loss_fn(end_logits, end_positions)
loss = start_loss + end_loss
loss.backward()
optimizer.step()
# update progress bar
pbar.set_postfix(loss=float(loss))
pbar.update(1)
# Validation: Keep track of the average loss and accuracy
model.eval()
val_loss, num_matches, num_batches, num_samples = 0, 0, 0, 0
with torch.no_grad():
for batch in val_loader:
# get batched data
input_ids, token_type_ids, start_positions, end_positions = batch
input_ids = input_ids.to(device)
token_type_ids = token_type_ids.to(device)
start_positions = start_positions.to(device)
end_positions = end_positions.to(device)
# forward pass on validation data
start_logits, end_logits = model(input_ids, token_type_ids)
# compute loss
start_loss = loss_fn(start_logits, start_positions)
end_loss = loss_fn(end_logits, end_positions)
loss = start_loss + end_loss
val_loss += loss.item()
num_batches += 1
# compute accuracy
pred_start = start_logits.argmax(dim=–1)
pred_end = end_logits.argmax(dim=–1)
match = (pred_start == start_positions) & (pred_end == end_positions)
num_matches += match.sum().item()
num_samples += len(start_positions)
avg_loss = val_loss / num_batches
acc = num_matches / num_samples
print(f“Validation {epoch+1}/{num_epochs}: acc {acc:.4f}, avg loss {avg_loss:.4f}”)
# Save the fine-tuned model
torch.save(model.state_dict(), f“bert_model_squad.pth”)
