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question_answeing.py
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question_answeing.py
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import argparse
import logging
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformers import AutoModel, AutoTokenizer, RobertaConfig, RobertaModel
from safetensors.torch import load_file
from src.models.longformer import Longformer
from src.models.config import LongformerConfig
from src.models.banded_gemm import pad_to_window_size
from src.utils.wikihop import WikihopQADataset
from src.trainer.trainer import Trainer
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def load_longformer(model_path):
config = LongformerConfig(
vocab_size=50265,
num_hidden_layers=6,
hidden_size=768,
num_attention_heads=12,
max_position_embeddings=2050,
attention_window=[256]*6,
attention_dilation=[1]*6,
num_labels=2,
type_vocab_size=1,
)
longformer = Longformer(config, add_pooling_layer=False)
state_dict = load_file(model_path)
state_dict = {k.replace("roberta.", ""): v for k, v in state_dict.items()}
longformer.load_state_dict(state_dict, strict=False)
current_embed = longformer.embeddings.word_embeddings.weight
current_vocab_size, embed_size = current_embed.size()
new_embed = longformer.embeddings.word_embeddings.weight.new_empty(current_vocab_size + 4, embed_size)
new_embed.normal_(mean=torch.mean(current_embed).item(), std=torch.std(current_embed).item())
new_embed[:current_vocab_size] = current_embed
longformer.embeddings.word_embeddings.num_embeddings = current_vocab_size + 4
del longformer.embeddings.word_embeddings.weight
longformer.embeddings.word_embeddings.weight = torch.nn.Parameter(new_embed)
print("Loaded model")
print(longformer)
return longformer
def load_distilroberta(model_name='distilbert/distilroberta-base'):
model = AutoModel.from_pretrained(model_name)
config = RobertaConfig.from_pretrained(model_name)
distil_roberta = RobertaModel(config, add_pooling_layer=False)
distil_roberta.load_state_dict(model.state_dict(), strict=False)
del model
model = distil_roberta
current_embed = model.embeddings.word_embeddings.weight
current_vocab_size, embed_size = current_embed.size()
new_embed = model.embeddings.word_embeddings.weight.new_empty(current_vocab_size + 4, embed_size)
new_embed.normal_(mean=torch.mean(current_embed).item(), std=torch.std(current_embed).item())
new_embed[:current_vocab_size] = current_embed
model.embeddings.word_embeddings.num_embeddings = current_vocab_size + 4
del model.embeddings.word_embeddings.weight
model.embeddings.word_embeddings.weight = torch.nn.Parameter(new_embed)
print("Loaded model")
print(model)
return model
def get_activations_longformer(model, candidate_ids, support_ids, max_seq_len, truncate_seq_len):
candidate_len = candidate_ids.shape[1]
support_len = support_ids.shape[1]
if candidate_len + support_len <= max_seq_len:
token_ids = torch.cat([candidate_ids, support_ids], dim=1)
attention_mask = torch.ones(token_ids.shape, dtype=torch.long, device=token_ids.device)
attention_mask[0, :candidate_len] = 2
token_ids, attention_mask = pad_to_window_size(
token_ids, attention_mask, model.config.attention_window[0], model.config.pad_token_id)
return [model(token_ids, attention_mask=attention_mask)[0]]
else:
all_activations = []
available_support_len = max_seq_len - candidate_len
for start in range(0, support_len, available_support_len):
end = min(start + available_support_len, support_len, truncate_seq_len)
token_ids = torch.cat([candidate_ids, support_ids[:, start:end]], dim=1)
attention_mask = torch.ones(token_ids.shape, dtype=torch.long, device=token_ids.device)
attention_mask[0, :candidate_len] = 2
token_ids, attention_mask = pad_to_window_size(
token_ids, attention_mask, model.config.attention_window[0], model.config.pad_token_id)
activations = model(token_ids, attention_mask=attention_mask)[0]
all_activations.append(activations)
if end == truncate_seq_len:
break
return all_activations
def pad_to_max_length(input_ids, attention_mask, max_length, pad_token_id):
padding_length = max_length - input_ids.size(1)
assert padding_length == max_length - attention_mask.size(1)
if padding_length > 0:
input_ids = torch.nn.functional.pad(input_ids, (0, padding_length), value=pad_token_id)
attention_mask = torch.nn.functional.pad(attention_mask, (0, padding_length), value=0)
return input_ids, attention_mask
def get_activations_roberta(model, candidate_ids, support_ids, max_seq_len, truncate_seq_len):
candidate_len = candidate_ids.shape[1]
support_len = support_ids.shape[1]
if candidate_len + support_len <= max_seq_len:
token_ids = torch.cat([candidate_ids, support_ids], dim=1)
attention_mask = torch.ones(token_ids.shape, dtype=torch.long, device=token_ids.device)
token_ids, attention_mask = pad_to_max_length(
token_ids, attention_mask, max_seq_len, model.config.pad_token_id)
return [model(token_ids, attention_mask=attention_mask)[0]]
else:
all_activations = []
available_support_len = max_seq_len - candidate_len
for start in range(0, support_len, available_support_len):
end = min(start + available_support_len, support_len, truncate_seq_len)
token_ids = torch.cat([candidate_ids, support_ids[:, start:end]], dim=1)
attention_mask = torch.ones(token_ids.shape, dtype=torch.long, device=token_ids.device)
token_ids, attention_mask = pad_to_max_length(
token_ids, attention_mask, max_seq_len, model.config.pad_token_id)
activations = model(token_ids, attention_mask=attention_mask)[0]
all_activations.append(activations)
if end == truncate_seq_len:
break
return all_activations
class WikihopQAModel(nn.Module):
def __init__(self, longformer=False, model_path=None):
super(WikihopQAModel, self).__init__()
self.longformer = longformer
if longformer:
self.model = load_longformer(model_path)
else:
self.model = load_distilroberta(model_path)
self.answer_score = torch.nn.Linear(self.model.embeddings.word_embeddings.weight.shape[1], 1, bias=False)
self.loss = torch.nn.CrossEntropyLoss(reduction='sum')
self._truncate_seq_len = 100000000000
def forward(self, data, return_predicted_index=False):
candidate_ids, support_ids, prediction_indices, correct_prediction_index = data
if self.longformer:
activations = get_activations_longformer(
self.model,
candidate_ids,
support_ids,
2048,
self._truncate_seq_len)
else:
activations = get_activations_roberta(
self.model,
candidate_ids,
support_ids,
512,
self._truncate_seq_len)
prediction_activations = [act.index_select(1, prediction_indices) for act in activations]
prediction_scores = [
self.answer_score(prediction_act).squeeze(-1)
for prediction_act in prediction_activations
]
average_prediction_scores = torch.cat(
[pred_scores.unsqueeze(-1) for pred_scores in prediction_scores], dim=-1
).mean(dim=-1)
loss = self.loss(average_prediction_scores, correct_prediction_index)
batch_size = candidate_ids.new_ones(1) * prediction_activations[0].shape[0]
predicted_answers = average_prediction_scores.argmax(dim=1)
num_correct = (predicted_answers == correct_prediction_index).int().sum()
if not return_predicted_index:
return loss, batch_size, num_correct
else:
return loss, batch_size, num_correct, predicted_answers
def train_step(self, batch, return_predicted_index=False):
output = self.forward(batch, return_predicted_index)
if return_predicted_index:
loss, batch_size, num_correct, predicted_answers = output
return loss, batch_size, num_correct, predicted_answers
else:
loss, batch_size, num_correct = output
return loss, batch_size, num_correct
def test_step(self, batch, return_predicted_index=False):
output = self.forward(batch, return_predicted_index)
if return_predicted_index:
loss, batch_size, num_correct, predicted_answers = output
return loss, batch_size, num_correct, predicted_answers
else:
loss, batch_size, num_correct = output
return loss, batch_size, num_correct
def main(args):
train_dataset = WikihopQADataset(args.train_data, shuffle_candidates=False, tokenize=True)
val_dataset = WikihopQADataset(args.val_data, shuffle_candidates=False, tokenize=True)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True, collate_fn=WikihopQADataset.collate_single_item)
test_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, collate_fn=WikihopQADataset.collate_single_item)
print(f"Length of train_loader: {len(train_loader)}")
print(f"Length of test_loader: {len(test_loader)}")
model = WikihopQAModel(longformer=args.longformer, model_path=args.model_path)
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.98))
epochs = args.epochs
batch_size = 1
num_examples = len(train_dataset)
training_steps = epochs * num_examples // batch_size
scheduler = optim.lr_scheduler.PolynomialLR(optimizer, total_iters=training_steps, power=1.0)
def compute_accuracy(batch, outputs):
_, batch_size, num_correct = outputs
return {"accuracy": num_correct.item()}
trainer = Trainer(
device="cuda" if torch.cuda.is_available() else "cpu",
default_root_dir=args.default_root_dir,
optimizer=optimizer,
scheduler=scheduler,
compute_metrics=compute_accuracy,
logger="wandb",
log=False,
max_epochs=epochs,
use_mixed_precision=True,
gradient_accumulation_steps=1,
warmup_steps=100,
val_check_interval=args.val_check_interval,
project_name=args.project_name,
)
trainer.train(model, train_loader, test_loader)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--train_data", type=str, default="data/wikihop/train.tokenized_2048.json")
parser.add_argument("--val_data", type=str, default="data/wikihop/dev.tokenized_2048.json")
parser.add_argument("--longformer", type=bool, default=False)
parser.add_argument("--model_path", type=str, default="./checkpoint-3000/model.safetensors")
parser.add_argument("--lr", type=float, default=3e-05)
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument("--epochs", type=int, default=5)
parser.add_argument("--default_root_dir", type=str, default="./model/")
parser.add_argument("--val_check_interval", type=int, default=10)
parser.add_argument("--project_name", type=str, default="WikihopQA")
args = parser.parse_args()
main(args)