Update trainer script
parent
0b622f29c2
commit
2847a2fa51
171
ft.py
171
ft.py
|
@ -4,85 +4,126 @@ import torch
|
||||||
from dl import load_dataset
|
from dl import load_dataset
|
||||||
from tqdm import tqdm
|
from tqdm import tqdm
|
||||||
|
|
||||||
# Enable cudnn optimizations
|
|
||||||
torch.backends.cudnn.benchmark = True
|
|
||||||
|
|
||||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
class FT:
|
||||||
|
def __init__(self):
|
||||||
|
# Enable cudnn optimizations
|
||||||
|
torch.backends.cudnn.benchmark = True
|
||||||
|
|
||||||
# load tokenizer and model
|
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||||
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
|
|
||||||
model = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
|
|
||||||
model.to(device)
|
|
||||||
|
|
||||||
# set up optimizer
|
# load tokenizer and model
|
||||||
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
|
self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
|
||||||
|
self.model = BartForConditionalGeneration.from_pretrained("facebook/bart-base")
|
||||||
|
self.model.to(self.device)
|
||||||
|
|
||||||
# Initialize Amp. This should be optional and should not affect computation if not available
|
# set up optimizer
|
||||||
try:
|
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=1e-5)
|
||||||
from torch.cuda.amp import GradScaler, autocast
|
|
||||||
scaler = GradScaler()
|
|
||||||
except ImportError:
|
|
||||||
# If Amp is not available, we'll simply define a dummy context manager
|
|
||||||
class autocast:
|
|
||||||
def __enter__(self):
|
|
||||||
pass
|
|
||||||
def __exit__(self, *args):
|
|
||||||
pass
|
|
||||||
scaler = None # We won't use a scaler if we don't have Amp
|
|
||||||
|
|
||||||
def train_model(dataloader):
|
try:
|
||||||
model.train()
|
from torch.cuda.amp import GradScaler, autocast
|
||||||
total_loss = 0
|
|
||||||
print("Training model...")
|
|
||||||
for batch in tqdm(dataloader):
|
|
||||||
optimizer.zero_grad()
|
|
||||||
|
|
||||||
inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
self.scaler = GradScaler()
|
||||||
inputs.to(device)
|
except ImportError:
|
||||||
labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
# If Amp is not available, we'll simply define a dummy context manager
|
||||||
labels.to(device)
|
class autocast:
|
||||||
|
def __enter__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
|
def __exit__(self, *args):
|
||||||
|
pass
|
||||||
|
|
||||||
loss = outputs.loss
|
self.scaler = None # We won't use a scaler if we don't have Amp
|
||||||
loss.backward()
|
|
||||||
|
|
||||||
optimizer.step()
|
def train_model(self, dataloader):
|
||||||
total_loss += loss.item()
|
self.model.train()
|
||||||
|
total_loss = 0
|
||||||
|
print("Training model...")
|
||||||
|
for batch in tqdm(dataloader):
|
||||||
|
self.optimizer.zero_grad()
|
||||||
|
|
||||||
avg_train_loss = total_loss / len(dataloader)
|
inputs = self.tokenizer(
|
||||||
return avg_train_loss
|
batch[1],
|
||||||
|
return_tensors="pt",
|
||||||
|
padding=True,
|
||||||
|
truncation=True,
|
||||||
|
max_length=512,
|
||||||
|
)
|
||||||
|
inputs.to(self.device)
|
||||||
|
labels = self.tokenizer(
|
||||||
|
batch[0],
|
||||||
|
return_tensors="pt",
|
||||||
|
padding=True,
|
||||||
|
truncation=True,
|
||||||
|
max_length=512,
|
||||||
|
)
|
||||||
|
labels.to(self.device)
|
||||||
|
|
||||||
|
outputs = self.model(
|
||||||
|
input_ids=inputs["input_ids"],
|
||||||
|
attention_mask=inputs["attention_mask"],
|
||||||
|
labels=labels["input_ids"],
|
||||||
|
)
|
||||||
|
|
||||||
def test_model(dataloader):
|
|
||||||
model.eval()
|
|
||||||
total_loss = 0
|
|
||||||
print("Testing model...")
|
|
||||||
for batch in tqdm(dataloader):
|
|
||||||
with torch.no_grad():
|
|
||||||
inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
|
||||||
inputs.to(device)
|
|
||||||
labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
|
||||||
labels.to(device)
|
|
||||||
outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
|
|
||||||
loss = outputs.loss
|
loss = outputs.loss
|
||||||
|
loss.backward()
|
||||||
|
|
||||||
|
self.optimizer.step()
|
||||||
total_loss += loss.item()
|
total_loss += loss.item()
|
||||||
|
|
||||||
avg_test_loss = total_loss / len(dataloader)
|
avg_train_loss = total_loss / len(dataloader)
|
||||||
return avg_test_loss
|
return avg_train_loss
|
||||||
|
|
||||||
|
def test_model(self, dataloader):
|
||||||
|
self.model.eval()
|
||||||
|
total_loss = 0
|
||||||
|
print("Testing model...")
|
||||||
|
for batch in tqdm(dataloader):
|
||||||
|
with torch.no_grad():
|
||||||
|
inputs = self.tokenizer(
|
||||||
|
batch[1],
|
||||||
|
return_tensors="pt",
|
||||||
|
padding=True,
|
||||||
|
truncation=True,
|
||||||
|
max_length=512,
|
||||||
|
)
|
||||||
|
inputs.to(self.device)
|
||||||
|
labels = self.tokenizer(
|
||||||
|
batch[0],
|
||||||
|
return_tensors="pt",
|
||||||
|
padding=True,
|
||||||
|
truncation=True,
|
||||||
|
max_length=512,
|
||||||
|
)
|
||||||
|
labels.to(self.device)
|
||||||
|
outputs = self.model(
|
||||||
|
input_ids=inputs["input_ids"],
|
||||||
|
attention_mask=inputs["attention_mask"],
|
||||||
|
labels=labels["input_ids"],
|
||||||
|
)
|
||||||
|
loss = outputs.loss
|
||||||
|
total_loss += loss.item()
|
||||||
|
|
||||||
|
avg_test_loss = total_loss / len(dataloader)
|
||||||
|
return avg_test_loss
|
||||||
|
|
||||||
|
def train(self):
|
||||||
|
train_dataloader, test_dataloader = load_dataset(
|
||||||
|
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
||||||
|
100,
|
||||||
|
100,
|
||||||
|
1,
|
||||||
|
test_ratio=0.2,
|
||||||
|
)
|
||||||
|
num_epochs = 3
|
||||||
|
for epoch in range(num_epochs):
|
||||||
|
avg_train_loss = self.train_model(train_dataloader)
|
||||||
|
print(f"Train loss for epoch {epoch+1}: {avg_train_loss}")
|
||||||
|
|
||||||
|
avg_test_loss = self.test_model(test_dataloader)
|
||||||
|
print(f"Test loss for epoch {epoch+1}: {avg_test_loss}")
|
||||||
|
|
||||||
def train():
|
|
||||||
train_dataloader, test_dataloader = load_dataset(
|
|
||||||
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
|
||||||
100, 100, 1, test_ratio=0.2
|
|
||||||
)
|
|
||||||
num_epochs = 3
|
|
||||||
for epoch in range(num_epochs):
|
|
||||||
avg_train_loss = train_model(train_dataloader)
|
|
||||||
print(f"Train loss for epoch {epoch+1}: {avg_train_loss}")
|
|
||||||
|
|
||||||
avg_test_loss = test_model(test_dataloader)
|
|
||||||
print(f"Test loss for epoch {epoch+1}: {avg_test_loss}")
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
train()
|
trainer = FT()
|
||||||
|
trainer.train()
|
||||||
|
|
Loading…
Reference in New Issue