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A full training

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# A full training

Now we’ll see how to achieve the same results as we did in the last section without using the Trainer class. Again, we assume you have done the data processing in section 2. Here is a short summary covering everything you will need:

from datasets import load_dataset

checkpoint = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

def tokenize_function(example):

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

### Prepare for training

Before actually writing our training loop, we will need to define a few objects. The first ones are the dataloaders we will use to iterate over batches. But before we can define those dataloaders, we need to apply a bit of postprocessing to our tokenized_datasets, to take care of some things that the Trainer did for us automatically. Specifically, we need to:

• Remove the columns corresponding to values the model does not expect (like the sentence1 and sentence2 columns).
• Rename the column label to labels (because the model expects the argument to be named labels).
• Set the format of the datasets so they return PyTorch tensors instead of lists.

Our tokenized_datasets has one method for each of those steps:

tokenized_datasets = tokenized_datasets.remove_columns(["sentence1", "sentence2", "idx"])
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
tokenized_datasets.set_format("torch")
tokenized_datasets["train"].column_names

We can then check that the result only has columns that our model will accept:

["attention_mask", "input_ids", "labels", "token_type_ids"]

Now that this is done, we can easily define our dataloaders:

from torch.utils.data import DataLoader

tokenized_datasets["train"], shuffle=True, batch_size=8, collate_fn=data_collator
)
tokenized_datasets["validation"], batch_size=8, collate_fn=data_collator
)

To quickly check there is no mistake in the data processing, we can inspect a batch like this:

for batch in train_dataloader:
break
{k: v.shape for k, v in batch.items()}
{'attention_mask': torch.Size([8, 65]),
'input_ids': torch.Size([8, 65]),
'labels': torch.Size([8]),
'token_type_ids': torch.Size([8, 65])}

Note that the actual shapes will probably be slightly different for you since we set shuffle=True for the training dataloader and we are padding to the maximum length inside the batch.

Now that we’re completely finished with data preprocessing (a satisfying yet elusive goal for any ML practitioner), let’s turn to the model. We instantiate it exactly as we did in the previous section:

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

To make sure that everything will go smoothly during training, we pass our batch to this model:

outputs = model(**batch)
print(outputs.loss, outputs.logits.shape)
tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])

All 🤗 Transformers models will return the loss when labels are provided, and we also get the logits (two for each input in our batch, so a tensor of size 8 x 2).

We’re almost ready to write our training loop! We’re just missing two things: an optimizer and a learning rate scheduler. Since we are trying to replicate what the Trainer was doing by hand, we will use the same defaults. The optimizer used by the Trainer is AdamW, which is the same as Adam, but with a twist for weight decay regularization (see “Decoupled Weight Decay Regularization” by Ilya Loshchilov and Frank Hutter):

from transformers import AdamW

optimizer = AdamW(model.parameters(), lr=5e-5)

Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5) to 0. To properly define it, we need to know the number of training steps we will take, which is the number of epochs we want to run multiplied by the number of training batches (which is the length of our training dataloader). The Trainer uses three epochs by default, so we will follow that:

from transformers import get_scheduler

num_epochs = 3
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)
print(num_training_steps)
1377

### The training loop

One last thing: we will want to use the GPU if we have access to one (on a CPU, training might take several hours instead of a couple of minutes). To do this, we define a device we will put our model and our batches on:

import torch

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
device
device(type='cuda')

We are now ready to train! To get some sense of when training will be finished, we add a progress bar over our number of training steps, using the tqdm library:

from tqdm.auto import tqdm

progress_bar = tqdm(range(num_training_steps))

model.train()
for epoch in range(num_epochs):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()

optimizer.step()
lr_scheduler.step()
progress_bar.update(1)

You can see that the core of the training loop looks a lot like the one in the introduction. We didn’t ask for any reporting, so this training loop will not tell us anything about how the model fares. We need to add an evaluation loop for that.

### The evaluation loop

As we did earlier, we will use a metric provided by the 🤗 Evaluate library. We’ve already seen the metric.compute() method, but metrics can actually accumulate batches for us as we go over the prediction loop with the method add_batch(). Once we have accumulated all the batches, we can get the final result with metric.compute(). Here’s how to implement all of this in an evaluation loop:

import evaluate

model.eval()
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)

logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)

metric.compute()
{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}

Again, your results will be slightly different because of the randomness in the model head initialization and the data shuffling, but they should be in the same ballpark.

✏️ Try it out! Modify the previous training loop to fine-tune your model on the SST-2 dataset.

### Supercharge your training loop with 🤗 Accelerate

The training loop we defined earlier works fine on a single CPU or GPU. But using the 🤗 Accelerate library, with just a few adjustments we can enable distributed training on multiple GPUs or TPUs. Starting from the creation of the training and validation dataloaders, here is what our manual training loop looks like:

from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)

num_epochs = 3
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)

progress_bar = tqdm(range(num_training_steps))

model.train()
for epoch in range(num_epochs):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()

optimizer.step()
lr_scheduler.step()
progress_bar.update(1)

And here are the changes:

+ from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

+ accelerator = Accelerator()

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- model.to(device)

+ )

num_epochs = 3
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)

progress_bar = tqdm(range(num_training_steps))

model.train()
for epoch in range(num_epochs):
-         batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
-         loss.backward()
+         accelerator.backward(loss)

optimizer.step()
lr_scheduler.step()
progress_bar.update(1)

The first line to add is the import line. The second line instantiates an Accelerator object that will look at the environment and initialize the proper distributed setup. 🤗 Accelerate handles the device placement for you, so you can remove the lines that put the model on the device (or, if you prefer, change them to use accelerator.device instead of device).

Then the main bulk of the work is done in the line that sends the dataloaders, the model, and the optimizer to accelerator.prepare(). This will wrap those objects in the proper container to make sure your distributed training works as intended. The remaining changes to make are removing the line that puts the batch on the device (again, if you want to keep this you can just change it to use accelerator.device) and replacing loss.backward() with accelerator.backward(loss).

⚠️ In order to benefit from the speed-up offered by Cloud TPUs, we recommend padding your samples to a fixed length with the padding="max_length" and max_length arguments of the tokenizer.

If you’d like to copy and paste it to play around, here’s what the complete training loop looks like with 🤗 Accelerate:

from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

accelerator = Accelerator()

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

train_dl, eval_dl, model, optimizer = accelerator.prepare(
)

num_epochs = 3
num_training_steps = num_epochs * len(train_dl)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)

progress_bar = tqdm(range(num_training_steps))

model.train()
for epoch in range(num_epochs):
for batch in train_dl:
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)

optimizer.step()
lr_scheduler.step()
progress_bar.update(1)

Putting this in a train.py script will make that script runnable on any kind of distributed setup. To try it out in your distributed setup, run the command:

accelerate config

which will prompt you to answer a few questions and dump your answers in a configuration file used by this command:

accelerate launch train.py

which will launch the distributed training.

If you want to try this in a Notebook (for instance, to test it with TPUs on Colab), just paste the code in a training_function() and run a last cell with:

from accelerate import notebook_launcher

notebook_launcher(training_function)

You can find more examples in the 🤗 Accelerate repo.