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Take a look at the Create a custom architecture guide for more information about building custom configurations.
Trainer - a PyTorch optimized training loop
All models are a standard torch.nn.Module so you can use them in any typical training loop. While you can write your own training loop, 🤗 Transformers provides a [Trainer] class for PyTorch, which contains the basic training loop and adds additional functionality for features like distributed training, mixed precision, and more.
Depending on your task, you'll typically pass the following parameters to [Trainer]: |
You'll start with a [PreTrainedModel] or a torch.nn.Module:
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
[TrainingArguments] contains the model hyperparameters you can change like learning rate, batch size, and the number of epochs to train for. The default values are used if you don't specify any training arguments: |
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir="path/to/save/folder/",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=2,
)
Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
Load a dataset: |
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
Load a dataset:
from datasets import load_dataset
dataset = load_dataset("rotten_tomatoes") # doctest: +IGNORE_RESULT
Create a function to tokenize the dataset:
def tokenize_dataset(dataset):
return tokenizer(dataset["text"])
Then apply it over the entire dataset with [~datasets.Dataset.map]:
dataset = dataset.map(tokenize_dataset, batched=True) |
Then apply it over the entire dataset with [~datasets.Dataset.map]:
dataset = dataset.map(tokenize_dataset, batched=True)
A [DataCollatorWithPadding] to create a batch of examples from your dataset:
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
Now gather all these classes in [Trainer]: |
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
Now gather all these classes in [Trainer]:
from transformers import Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
tokenizer=tokenizer,
data_collator=data_collator,
) # doctest: +SKIP
When you're ready, call [~Trainer.train] to start training:
trainer.train() # doctest: +SKIP |
When you're ready, call [~Trainer.train] to start training:
trainer.train() # doctest: +SKIP
For tasks - like translation or summarization - that use a sequence-to-sequence model, use the [Seq2SeqTrainer] and [Seq2SeqTrainingArguments] classes instead. |
You can customize the training loop behavior by subclassing the methods inside [Trainer]. This allows you to customize features such as the loss function, optimizer, and scheduler. Take a look at the [Trainer] reference for which methods can be subclassed.
The other way to customize the training loop is by using Callbacks. You can use callbacks to integrate with other libraries and inspect the training loop to report on progress or stop the training early. Callbacks do not modify anything in the training loop itself. To customize something like the loss function, you need to subclass the [Trainer] instead.
Train with TensorFlow
All models are a standard tf.keras.Model so they can be trained in TensorFlow with the Keras API. 🤗 Transformers provides the [~TFPreTrainedModel.prepare_tf_dataset] method to easily load your dataset as a tf.data.Dataset so you can start training right away with Keras' compile and fit methods. |
You'll start with a [TFPreTrainedModel] or a tf.keras.Model:
from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
Create a function to tokenize the dataset: |
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
Create a function to tokenize the dataset:
def tokenize_dataset(dataset):
return tokenizer(dataset["text"]) # doctest: +SKIP
Apply the tokenizer over the entire dataset with [~datasets.Dataset.map] and then pass the dataset and tokenizer to [~TFPreTrainedModel.prepare_tf_dataset]. You can also change the batch size and shuffle the dataset here if you'd like: |
dataset = dataset.map(tokenize_dataset) # doctest: +SKIP
tf_dataset = model.prepare_tf_dataset(
dataset["train"], batch_size=16, shuffle=True, tokenizer=tokenizer
) # doctest: +SKIP
When you're ready, you can call compile and fit to start training. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: |
When you're ready, you can call compile and fit to start training. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(3e-5)) # No loss argument!
model.fit(tf_dataset) # doctest: +SKIP |
What's next?
Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides! |
Trainer
The [Trainer] is a complete training and evaluation loop for PyTorch models implemented in the Transformers library. You only need to pass it the necessary pieces for training (model, tokenizer, dataset, evaluation function, training hyperparameters, etc.), and the [Trainer] class takes care of the rest. This makes it easier to start training faster without manually writing your own training loop. But at the same time, [Trainer] is very customizable and offers a ton of training options so you can tailor it to your exact training needs. |
In addition to the [Trainer] class, Transformers also provides a [Seq2SeqTrainer] class for sequence-to-sequence tasks like translation or summarization. There is also the [~trl.SFTTrainer] class from the TRL library which wraps the [Trainer] class and is optimized for training language models like Llama-2 and Mistral with autoregressive techniques. [~trl.SFTTrainer] also supports features like sequence packing, LoRA, quantization, and DeepSpeed for efficiently scaling to any model size. |
Feel free to check out the API reference for these other [Trainer]-type classes to learn more about when to use which one. In general, [Trainer] is the most versatile option and is appropriate for a broad spectrum of tasks. [Seq2SeqTrainer] is designed for sequence-to-sequence tasks and [~trl.SFTTrainer] is designed for training language models. |
Before you start, make sure Accelerate - a library for enabling and running PyTorch training across distributed environments - is installed.
```bash
pip install accelerate
upgrade
pip install accelerate --upgrade
This guide provides an overview of the [Trainer] class.
Basic usage
[Trainer] includes all the code you'll find in a basic training loop: |
This guide provides an overview of the [Trainer] class.
Basic usage
[Trainer] includes all the code you'll find in a basic training loop:
perform a training step to calculate the loss
calculate the gradients with the [~accelerate.Accelerator.backward] method
update the weights based on the gradients
repeat this process until you've reached a predetermined number of epochs |
The [Trainer] class abstracts all of this code away so you don't have to worry about manually writing a training loop every time or if you're just getting started with PyTorch and training. You only need to provide the essential components required for training, such as a model and a dataset, and the [Trainer] class handles everything else.
If you want to specify any training options or hyperparameters, you can find them in the [TrainingArguments] class. For example, let's define where to save the model in output_dir and push the model to the Hub after training with push_to_hub=True. |
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir="your-model",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=2,
weight_decay=0.01,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
push_to_hub=True,
) |
Pass training_args to the [Trainer] along with a model, dataset, something to preprocess the dataset with (depending on your data type it could be a tokenizer, feature extractor or image processor), a data collator, and a function to compute the metrics you want to track during training.
Finally, call [~Trainer.train] to start training! |
from transformers import Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics,
)
trainer.train() |
Checkpoints
The [Trainer] class saves your model checkpoints to the directory specified in the output_dir parameter of [TrainingArguments]. You'll find the checkpoints saved in a checkpoint-000 subfolder where the numbers at the end correspond to the training step. Saving checkpoints are useful for resuming training later. |
resume from latest checkpoint
trainer.train(resume_from_checkpoint=True)
resume from specific checkpoint saved in output directory
trainer.train(resume_from_checkpoint="your-model/checkpoint-1000")
You can save your checkpoints (the optimizer state is not saved by default) to the Hub by setting push_to_hub=True in [TrainingArguments] to commit and push them. Other options for deciding how your checkpoints are saved are set up in the hub_strategy parameter: |
hub_strategy="checkpoint" pushes the latest checkpoint to a subfolder named "last-checkpoint" from which you can resume training
hub_strategy="all_checkpoints" pushes all checkpoints to the directory defined in output_dir (you'll see one checkpoint per folder in your model repository) |
When you resume training from a checkpoint, the [Trainer] tries to keep the Python, NumPy, and PyTorch RNG states the same as they were when the checkpoint was saved. But because PyTorch has various non-deterministic default settings, the RNG states aren't guaranteed to be the same. If you want to enable full determinism, take a look at the Controlling sources of randomness guide to learn what you can enable to make your training fully deterministic. Keep in mind though that by making certain settings deterministic, training may be slower.
Customize the Trainer
While the [Trainer] class is designed to be accessible and easy-to-use, it also offers a lot of customizability for more adventurous users. Many of the [Trainer]'s method can be subclassed and overridden to support the functionality you want, without having to rewrite the entire training loop from scratch to accommodate it. These methods include: |
[~Trainer.get_train_dataloader] creates a training DataLoader
[~Trainer.get_eval_dataloader] creates an evaluation DataLoader
[~Trainer.get_test_dataloader] creates a test DataLoader
[~Trainer.log] logs information on the various objects that watch training
[~Trainer.create_optimizer_and_scheduler] creates an optimizer and learning rate scheduler if they weren't passed in the __init__; these can also be separately customized with [~Trainer.create_optimizer] and [~Trainer.create_scheduler] respectively
[~Trainer.compute_loss] computes the loss on a batch of training inputs
[~Trainer.training_step] performs the training step
[~Trainer.prediction_step] performs the prediction and test step
[~Trainer.evaluate] evaluates the model and returns the evaluation metrics
[~Trainer.predict] makes predictions (with metrics if labels are available) on the test set |
For example, if you want to customize the [~Trainer.compute_loss] method to use a weighted loss instead. |
from torch import nn
from transformers import Trainer
class CustomTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.pop("labels")
# forward pass
outputs = model(**inputs)
logits = outputs.get("logits")
# compute custom loss for 3 labels with different weights
loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
return (loss, outputs) if return_outputs else loss |
Callbacks
Another option for customizing the [Trainer] is to use callbacks. Callbacks don't change anything in the training loop. They inspect the training loop state and then execute some action (early stopping, logging results, etc.) depending on the state. In other words, a callback can't be used to implement something like a custom loss function and you'll need to subclass and override the [~Trainer.compute_loss] method for that.
For example, if you want to add an early stopping callback to the training loop after 10 steps. |
from transformers import TrainerCallback
class EarlyStoppingCallback(TrainerCallback):
def init(self, num_steps=10):
self.num_steps = num_steps
def on_step_end(self, args, state, control, **kwargs):
if state.global_step >= self.num_steps:
return {"should_training_stop": True}
else:
return {}
Then pass it to the [Trainer]'s callback parameter. |
Then pass it to the [Trainer]'s callback parameter.
from transformers import Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics,
callback=[EarlyStoppingCallback()],
)
Logging
Check out the logging API reference for more information about the different logging levels. |
The [Trainer] is set to logging.INFO by default which reports errors, warnings, and other basic information. A [Trainer] replica - in distributed environments - is set to logging.WARNING which only reports errors and warnings. You can change the logging level with the log_level and log_level_replica parameters in [TrainingArguments].
To configure the log level setting for each node, use the log_on_each_node parameter to determine whether to use the log level on each node or only on the main node. |
[Trainer] sets the log level separately for each node in the [Trainer.__init__] method, so you may want to consider setting this sooner if you're using other Transformers functionalities before creating the [Trainer] object.
For example, to set your main code and modules to use the same log level according to each node: |
For example, to set your main code and modules to use the same log level according to each node:
logger = logging.getLogger(name)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
trainer = Trainer() |
Use different combinations of log_level and log_level_replica to configure what gets logged on each of the nodes.
my_app.py --log_level warning --log_level_replica error
Add the log_on_each_node 0 parameter for multi-node environments.
```bash
my_app.py --log_level warning --log_level_replica error --log_on_each_node 0
set to only report errors
my_app.py --log_level error --log_level_replica error --log_on_each_node 0 |
NEFTune
NEFTune is a technique that can improve performance by adding noise to the embedding vectors during training. To enable it in [Trainer], set the neftune_noise_alpha parameter in [TrainingArguments] to control how much noise is added.
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(, neftune_noise_alpha=0.1)
trainer = Trainer(, args=training_args) |
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(, neftune_noise_alpha=0.1)
trainer = Trainer(, args=training_args)
NEFTune is disabled after training to restore the original embedding layer to avoid any unexpected behavior.
Accelerate and Trainer
The [Trainer] class is powered by Accelerate, a library for easily training PyTorch models in distributed environments with support for integrations such as FullyShardedDataParallel (FSDP) and DeepSpeed. |
Learn more about FSDP sharding strategies, CPU offloading, and more with the [Trainer] in the Fully Sharded Data Parallel guide.
To use Accelerate with [Trainer], run the accelerate.config command to set up training for your training environment. This command creates a config_file.yaml that'll be used when you launch your training script. For example, some example configurations you can setup are: |
yml
compute_environment: LOCAL_MACHINE
distributed_type: MULTI_GPU
downcast_bf16: 'no'
gpu_ids: all
machine_rank: 0 #change rank as per the node
main_process_ip: 192.168.20.1
main_process_port: 9898
main_training_function: main
mixed_precision: fp16
num_machines: 2
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false |
yml
compute_environment: LOCAL_MACHINE
distributed_type: FSDP
downcast_bf16: 'no'
fsdp_config:
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch_policy: BACKWARD_PRE
fsdp_forward_prefetch: true
fsdp_offload_params: false
fsdp_sharding_strategy: 1
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sync_module_states: true
fsdp_transformer_layer_cls_to_wrap: BertLayer
fsdp_use_orig_params: true
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false |
yml
compute_environment: LOCAL_MACHINE
deepspeed_config:
deepspeed_config_file: /home/user/configs/ds_zero3_config.json
zero3_init_flag: true
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false |
yml
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 0.7
offload_optimizer_device: cpu
offload_param_device: cpu
zero3_init_flag: true
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false |
The accelerate_launch command is the recommended way to launch your training script on a distributed system with Accelerate and [Trainer] with the parameters specified in config_file.yaml. This file is saved to the Accelerate cache folder and automatically loaded when you run accelerate_launch.
For example, to run the run_glue.py training script with the FSDP configuration: |
accelerate launch \
./examples/pytorch/text-classification/run_glue.py \
--model_name_or_path google-bert/bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir
You could also specify the parameters from the config_file.yaml file directly in the command line: |
accelerate launch --num_processes=2 \
--use_fsdp \
--mixed_precision=bf16 \
--fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP \
--fsdp_transformer_layer_cls_to_wrap="BertLayer" \
--fsdp_sharding_strategy=1 \
--fsdp_state_dict_type=FULL_STATE_DICT \
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path google-bert/bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir
Check out the Launching your Accelerate scripts tutorial to learn more about accelerate_launch and custom configurations. |
Train with a script
Along with the 🤗 Transformers notebooks, there are also example scripts demonstrating how to train a model for a task with PyTorch, TensorFlow, or JAX/Flax.
You will also find scripts we've used in our research projects and legacy examples which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
The example scripts are not expected to work out-of-the-box on every problem, and you may need to adapt the script to the problem you're trying to solve. To help you with this, most of the scripts fully expose how data is preprocessed, allowing you to edit it as necessary for your use case.
For any feature you'd like to implement in an example script, please discuss it on the forum or in an issue before submitting a Pull Request. While we welcome bug fixes, it is unlikely we will merge a Pull Request that adds more functionality at the cost of readability.
This guide will show you how to run an example summarization training script in PyTorch and TensorFlow. All examples are expected to work with both frameworks unless otherwise specified.
Setup
To successfully run the latest version of the example scripts, you have to install 🤗 Transformers from source in a new virtual environment: |
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
For older versions of the example scripts, click on the toggle below:
Examples for older versions of 🤗 Transformers
v4.5.1
v4.4.2
v4.3.3
v4.2.2
v4.1.1
v4.0.1
v3.5.1
v3.4.0
v3.3.1
v3.2.0
v3.1.0
v3.0.2
v2.11.0
v2.10.0
v2.9.1
v2.8.0
v2.7.0
v2.6.0
v2.5.1
v2.4.0
v2.3.0
v2.2.0
v2.1.1
v2.0.0
v1.2.0
v1.1.0
v1.0.0
Then switch your current clone of 🤗 Transformers to a specific version, like v3.5.1 for example: |
Then switch your current clone of 🤗 Transformers to a specific version, like v3.5.1 for example:
git checkout tags/v3.5.1
After you've setup the correct library version, navigate to the example folder of your choice and install the example specific requirements:
pip install -r requirements.txt
Run a script |
pip install -r requirements.txt
Run a script
The example script downloads and preprocesses a dataset from the 🤗 Datasets library. Then the script fine-tunes a dataset with the Trainer on an architecture that supports summarization. The following example shows how to fine-tune T5-small on the CNN/DailyMail dataset. The T5 model requires an additional source_prefix argument due to how it was trained. This prompt lets T5 know this is a summarization task. |
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate |
The example script downloads and preprocesses a dataset from the 🤗 Datasets library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune T5-small on the CNN/DailyMail dataset. The T5 model requires an additional source_prefix argument due to how it was trained. This prompt lets T5 know this is a summarization task. |
python examples/tensorflow/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval |
Distributed training and mixed precision
The Trainer supports distributed training and mixed precision, which means you can also use it in a script. To enable both of these features:
Add the fp16 argument to enable mixed precision.
Set the number of GPUs to use with the nproc_per_node argument. |
torchrun \
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
--fp16 \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
TensorFlow scripts utilize a MirroredStrategy for distributed training, and you don't need to add any additional arguments to the training script. The TensorFlow script will use multiple GPUs by default if they are available.
Run a script on a TPU |
Tensor Processing Units (TPUs) are specifically designed to accelerate performance. PyTorch supports TPUs with the XLA deep learning compiler (see here for more details). To use a TPU, launch the xla_spawn.py script and use the num_cores argument to set the number of TPU cores you want to use. |
python xla_spawn.py --num_cores 8 \
summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate |
Tensor Processing Units (TPUs) are specifically designed to accelerate performance. TensorFlow scripts utilize a TPUStrategy for training on TPUs. To use a TPU, pass the name of the TPU resource to the tpu argument. |
python run_summarization.py \
--tpu name_of_tpu_resource \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval |
Run a script with 🤗 Accelerate
🤗 Accelerate is a PyTorch-only library that offers a unified method for training a model on several types of setups (CPU-only, multiple GPUs, TPUs) while maintaining complete visibility into the PyTorch training loop. Make sure you have 🤗 Accelerate installed if you don't already have it:
Note: As Accelerate is rapidly developing, the git version of accelerate must be installed to run the scripts
pip install git+https://github.com/huggingface/accelerate |
Note: As Accelerate is rapidly developing, the git version of accelerate must be installed to run the scripts
pip install git+https://github.com/huggingface/accelerate
Instead of the run_summarization.py script, you need to use the run_summarization_no_trainer.py script. 🤗 Accelerate supported scripts will have a task_no_trainer.py file in the folder. Begin by running the following command to create and save a configuration file: |
accelerate config
Test your setup to make sure it is configured correctly:
accelerate test
Now you are ready to launch the training: |
accelerate test
Now you are ready to launch the training:
accelerate launch run_summarization_no_trainer.py \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir ~/tmp/tst-summarization
Use a custom dataset
The summarization script supports custom datasets as long as they are a CSV or JSON Line file. When you use your own dataset, you need to specify several additional arguments: |
train_file and validation_file specify the path to your training and validation files.
text_column is the input text to summarize.
summary_column is the target text to output.
A summarization script using a custom dataset would look like this: |
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--train_file path_to_csv_or_jsonlines_file \
--validation_file path_to_csv_or_jsonlines_file \
--text_column text_column_name \
--summary_column summary_column_name \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--overwrite_output_dir \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--predict_with_generate
Test a script
It is often a good idea to run your script on a smaller number of dataset examples to ensure everything works as expected before committing to an entire dataset which may take hours to complete. Use the following arguments to truncate the dataset to a maximum number of samples: |
max_train_samples
max_eval_samples
max_predict_samples |
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--max_train_samples 50 \
--max_eval_samples 50 \
--max_predict_samples 50 \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
Not all example scripts support the max_predict_samples argument. If you aren't sure whether your script supports this argument, add the -h argument to check: |
examples/pytorch/summarization/run_summarization.py -h
Resume training from checkpoint
Another helpful option to enable is resuming training from a previous checkpoint. This will ensure you can pick up where you left off without starting over if your training gets interrupted. There are two methods to resume training from a checkpoint.
The first method uses the output_dir previous_output_dir argument to resume training from the latest checkpoint stored in output_dir. In this case, you should remove overwrite_output_dir: |
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--output_dir previous_output_dir \
--predict_with_generate
The second method uses the resume_from_checkpoint path_to_specific_checkpoint argument to resume training from a specific checkpoint folder. |
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--resume_from_checkpoint path_to_specific_checkpoint \
--predict_with_generate
Share your model
All scripts can upload your final model to the Model Hub. Make sure you are logged into Hugging Face before you begin: |
huggingface-cli login
Then add the push_to_hub argument to the script. This argument will create a repository with your Hugging Face username and the folder name specified in output_dir.
To give your repository a specific name, use the push_to_hub_model_id argument to add it. The repository will be automatically listed under your namespace.
The following example shows how to upload a model with a specific repository name: |
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--push_to_hub \
--push_to_hub_model_id finetuned-t5-cnn_dailymail \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate |
Building custom models
The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder
of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs.
If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you
how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it
with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗
Transformers library. We'll see how to build upon transformers and extend the framework with your hooks and
custom code.
We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the
timm library into a [PreTrainedModel].
Writing a custom configuration
Before we dive into the model, let's first write its configuration. The configuration of a model is an object that
will contain all the necessary information to build the model. As we will see in the next section, the model can only
take a config to be initialized, so we really need that object to be as complete as possible. |
Models in the transformers library itself generally follow the convention that they accept a config object
in their __init__ method, and then pass the whole config to sub-layers in the model, rather than breaking the
config object into multiple arguments that are all passed individually to sub-layers. Writing your model in this
style results in simpler code with a clear "source of truth" for any hyperparameters, and also makes it easier
to reuse code from other models in transformers. |
In our example, we will take a couple of arguments of the ResNet class that we might want to tweak. Different
configurations will then give us the different types of ResNets that are possible. We then just store those arguments,
after checking the validity of a few of them.
thon
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self,
block_type="bottleneck",
layers: List[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.") |
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs) |
The three important things to remember when writing you own configuration are the following:
- you have to inherit from PretrainedConfig,
- the __init__ of your PretrainedConfig must accept any kwargs,
- those kwargs need to be passed to the superclass __init__.
The inheritance is to make sure you get all the functionality from the 🤗 Transformers library, while the two other
constraints come from the fact a PretrainedConfig has more fields than the ones you are setting. When reloading a
config with the from_pretrained method, those fields need to be accepted by your config and then sent to the
superclass.
Defining a model_type for your configuration (here model_type="resnet") is not mandatory, unless you want to
register your model with the auto classes (see last section).
With this done, you can easily create and save your configuration like you would do with any other model config of the
library. Here is how we can create a resnet50d config and save it:
py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")
This will save a file named config.json inside the folder custom-resnet. You can then reload your config with the
from_pretrained method:
py
resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
You can also use any other method of the [PretrainedConfig] class, like [~PretrainedConfig.push_to_hub] to
directly upload your config to the Hub.
Writing a custom model
Now that we have our ResNet configuration, we can go on writing the model. We will actually write two: one that
extracts the hidden features from a batch of images (like [BertModel]) and one that is suitable for image
classification (like [BertForSequenceClassification]).
As we mentioned before, we'll only write a loose wrapper of the model to keep it simple for this example. The only
thing we need to do before writing this class is a map between the block types and actual block classes. Then the
model is defined from the configuration by passing everything to the ResNet class: |
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
) |
def forward(self, tensor):
return self.model.forward_features(tensor)
For the model that will classify images, we just change the forward method: |
import torch
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
) |
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = torch.nn.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits} |
In both cases, notice how we inherit from PreTrainedModel and call the superclass initialization with the config
(a bit like when you write a regular torch.nn.Module). The line that sets the config_class is not mandatory, unless
you want to register your model with the auto classes (see last section).
If your model is very similar to a model inside the library, you can re-use the same configuration as this model. |
You can have your model return anything you want, but returning a dictionary like we did for
ResnetModelForImageClassification, with the loss included when labels are passed, will make your model directly
usable inside the [Trainer] class. Using another output format is fine as long as you are planning on using your own
training loop or another library for training.
Now that we have our model class, let's create one:
py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
Again, you can use any of the methods of [PreTrainedModel], like [~PreTrainedModel.save_pretrained] or
[~PreTrainedModel.push_to_hub]. We will use the second in the next section, and see how to push the model weights
with the code of our model. But first, let's load some pretrained weights inside our model.
In your own use case, you will probably be training your custom model on your own data. To go fast for this tutorial,
we will use the pretrained version of the resnet50d. Since our model is just a wrapper around it, it's going to be
easy to transfer those weights: |
import timm
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict()) |
Now let's see how to make sure that when we do [~PreTrainedModel.save_pretrained] or [~PreTrainedModel.push_to_hub], the
code of the model is saved.
Registering a model with custom code to the auto classes
If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own
model. This is different from pushing the code to the Hub in the sense that users will need to import your library to
get the custom models (contrarily to automatically downloading the model code from the Hub).
As long as your config has a model_type attribute that is different from existing model types, and that your model
classes have the right config_class attributes, you can just add them to the auto classes like this: |
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification) |
Note that the first argument used when registering your custom config to [AutoConfig] needs to match the model_type
of your custom config, and the first argument used when registering your custom models to any auto model class needs
to match the config_class of those models.
Sending the code to the Hub
This API is experimental and may have some slight breaking changes in the next releases. |
First, make sure your model is fully defined in a .py file. It can rely on relative imports to some other files as
long as all the files are in the same directory (we don't support submodules for this feature yet). For our example,
we'll define a modeling_resnet.py file and a configuration_resnet.py file in a folder of the current working
directory named resnet_model. The configuration file contains the code for ResnetConfig and the modeling file
contains the code of ResnetModel and ResnetModelForImageClassification.
.
└── resnet_model
├── __init__.py
├── configuration_resnet.py
└── modeling_resnet.py
The __init__.py can be empty, it's just there so that Python detects resnet_model can be use as a module. |
If copying a modeling files from the library, you will need to replace all the relative imports at the top of the file
to import from the transformers package. |
Note that you can re-use (or subclass) an existing configuration/model.
To share your model with the community, follow those steps: first import the ResNet model and config from the newly
created files:
py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
Then you have to tell the library you want to copy the code files of those objects when using the save_pretrained
method and properly register them with a given Auto class (especially for models), just run:
py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")
Note that there is no need to specify an auto class for the configuration (there is only one auto class for them,
[AutoConfig]) but it's different for models. Your custom model could be suitable for many different tasks, so you
have to specify which one of the auto classes is the correct one for your model. |
Use register_for_auto_class() if you want the code files to be copied. If you instead prefer to use code on the Hub from another repo,
you don't need to call it. In cases where there's more than one auto class, you can modify the config.json directly using the
following structure:
json
"auto_map": {
"AutoConfig": "<your-repo-name>--<config-name>",
"AutoModel": "<your-repo-name>--<config-name>",
"AutoModelFor<Task>": "<your-repo-name>--<config-name>",
}, |
Next, let's create the config and models as we did before:
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
Now to send the model to the Hub, make sure you are logged in. Either run in your terminal:
huggingface-cli login
or from a notebook: |
Now to send the model to the Hub, make sure you are logged in. Either run in your terminal:
huggingface-cli login
or from a notebook:
from huggingface_hub import notebook_login
notebook_login() |
You can then push to your own namespace (or an organization you are a member of) like this:
py
resnet50d.push_to_hub("custom-resnet50d")
On top of the modeling weights and the configuration in json format, this also copied the modeling and
configuration .py files in the folder custom-resnet50d and uploaded the result to the Hub. You can check the result
in this model repo.
See the sharing tutorial for more information on the push to Hub method.
Using a model with custom code
You can use any configuration, model or tokenizer with custom code files in its repository with the auto-classes and
the from_pretrained method. All files and code uploaded to the Hub are scanned for malware (refer to the Hub security documentation for more information), but you should still
review the model code and author to avoid executing malicious code on your machine. Set trust_remote_code=True to use
a model with custom code: |
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True) |
It is also strongly encouraged to pass a commit hash as a revision to make sure the author of the models did not
update the code with some malicious new lines (unless you fully trust the authors of the models).
py
commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
"sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash
)
Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit
hash of any commit. |
Load pretrained instances with an AutoClass
With so many different Transformer architectures, it can be challenging to create one for your checkpoint. As a part of 🤗 Transformers core philosophy to make the library easy, simple and flexible to use, an AutoClass automatically infers and loads the correct architecture from a given checkpoint. The from_pretrained() method lets you quickly load a pretrained model for any architecture so you don't have to devote time and resources to train a model from scratch. Producing this type of checkpoint-agnostic code means if your code works for one checkpoint, it will work with another checkpoint - as long as it was trained for a similar task - even if the architecture is different. |
Remember, architecture refers to the skeleton of the model and checkpoints are the weights for a given architecture. For example, BERT is an architecture, while google-bert/bert-base-uncased is a checkpoint. Model is a general term that can mean either architecture or checkpoint.
In this tutorial, learn to:
Load a pretrained tokenizer.
Load a pretrained image processor
Load a pretrained feature extractor.
Load a pretrained processor.
Load a pretrained model.
Load a model as a backbone. |
Load a pretrained tokenizer.
Load a pretrained image processor
Load a pretrained feature extractor.
Load a pretrained processor.
Load a pretrained model.
Load a model as a backbone.
AutoTokenizer
Nearly every NLP task begins with a tokenizer. A tokenizer converts your input into a format that can be processed by the model.
Load a tokenizer with [AutoTokenizer.from_pretrained]:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") |
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
Then tokenize your input as shown below:
sequence = "In a hole in the ground there lived a hobbit."
print(tokenizer(sequence))
{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} |