How to use GPU for training instead of CPU? - pytorch

Question

I was replicating the code which is fine-tuned for Domain Adaptation. This is the main link to the post for more details:
(https://towardsdatascience.com/fine-tuning-for-domain-adaptation-in-nlp-c47def356fd6)
The code is as such:
!pip install -q transformers
!pip install -q datasets
import multiprocessing
import pandas as pd
import numpy as np
import torch
import matplotlib.pyplot as plt
import transformers
from datasets import Dataset
from sklearn.model_selection import train_test_split
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, AutoConfig
from transformers import BertForMaskedLM, DistilBertForMaskedLM
from transformers import BertTokenizer, DistilBertTokenizer
from transformers import RobertaTokenizer, RobertaForMaskedLM
from transformers import Trainer, TrainingArguments
from transformers import DataCollatorForLanguageModeling
from tokenizers import BertWordPieceTokenizer
# HYPERPARAMS
SEED_SPLIT = 0
SEED_TRAIN = 0
MAX_SEQ_LEN = 128
TRAIN_BATCH_SIZE = 16
EVAL_BATCH_SIZE = 16
LEARNING_RATE = 2e-5
LR_WARMUP_STEPS = 100
WEIGHT_DECAY = 0.01
# load data
dtf_mlm = pd.read_csv('data/jigsaw_train.csv', nrows=1000)
dtf_mlm = dtf_mlm[dtf_mlm["target"] < 0.5]
dtf_mlm = dtf_mlm.rename(columns={"comment_text": "text"})
# Train/Valid Split
df_train, df_valid = train_test_split(
dtf_mlm, test_size=0.15, random_state=SEED_SPLIT
)
len(df_train), len(df_valid)
# Convert to Dataset object
train_dataset = Dataset.from_pandas(df_train[['text']].dropna())
valid_dataset = Dataset.from_pandas(df_valid[['text']].dropna())
#Model Selection Part
MODEL = 'bert'
bert_type = 'bert-base-cased'
TokenizerClass = BertTokenizer
ModelClass = BertForMaskedLM
#Tokenization Part
tokenizer = TokenizerClass.from_pretrained(
bert_type, use_fast=True, do_lower_case=False, max_len=MAX_SEQ_LEN
)
model = ModelClass.from_pretrained(bert_type)
def tokenize_function(row):
return tokenizer(
row['text'],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LEN,
return_special_tokens_mask=True)
column_names = train_dataset.column_names
train_dataset = train_dataset.map(
tokenize_function,
batched=True,
num_proc=multiprocessing.cpu_count(),
remove_columns=column_names,
)
valid_dataset = valid_dataset.map(
tokenize_function,
batched=True,
num_proc=multiprocessing.cpu_count(),
remove_columns=column_names,
)
#Training and Model Saving Part
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)
steps_per_epoch = int(len(train_dataset) / TRAIN_BATCH_SIZE)
training_args = TrainingArguments(
output_dir='./bert-news',
logging_dir='./LMlogs',
num_train_epochs=2,
do_train=True,
do_eval=True,
per_device_train_batch_size=TRAIN_BATCH_SIZE,
per_device_eval_batch_size=EVAL_BATCH_SIZE,
warmup_steps=LR_WARMUP_STEPS,
save_steps=steps_per_epoch,
save_total_limit=3,
weight_decay=WEIGHT_DECAY,
learning_rate=LEARNING_RATE,
evaluation_strategy='epoch',
save_strategy='epoch',
load_best_model_at_end=True,
metric_for_best_model='loss',
greater_is_better=False,
seed=SEED_TRAIN
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=valid_dataset,
tokenizer=tokenizer,
)
trainer.train()
trainer.save_model("SavedModel/TestModel") #save your custom model
And this is the GPU that I am using:
I want to use the GPU for training the model on about 1.5 million comments.
I tried doing this:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#Setting the tokenizer and the model
tokenizer = TokenizerClass.from_pretrained(
bert_type, use_fast=True, do_lower_case=False, max_len=MAX_SEQ_LEN
)
model = ModelClass.from_pretrained(bert_type).to(device)
But I am unsure how to send the inputs and tokens to the GPU.
Feel free to give your advice, and I don't owe this code, shout out to Marcello Politi. Thanks!

Answer 1

After you load the dataset you should add:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
train_dataset = train_dataset.to(device)