lightning-transformers 0.2.5

Lightning Transformers.

Deprecation notice 🔒

This repository has been archived (read-only) on Nov 21, 2022. Thanks to everyone who contributed to lightning-transformers, we feel it's time to move on.

:hugs: Transformers can already be easily trained using the Lightning :zap: Trainer. Here's a recent example from the community: https://sachinruk.github.io/blog/deep-learning/2022/11/07/t5-for-grammar-correction.html. Note that there are no limitations or workarounds, things just work out of the box.

The lightning-transformers repo explored the possibility to provide task-specific modules and pre-baked defaults, at the cost of introducing extra abstractions. In the spirit of keeping ourselves focused, these abstractions are not something we wish to continue supporting.

If you liked lightning-transformers and want to continue developing it in the future, feel free to fork the repo and choose another name for the project.

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Flexible components pairing :hugs: Transformers with Pytorch Lightning :zap:

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Docs • Community

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Installation

pip install lightning-transformers

From Source

git clone https://github.com/PyTorchLightning/lightning-transformers.git
cd lightning-transformers
pip install .

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What is Lightning-Transformers

Lightning Transformers provides LightningModules, LightningDataModules and Strategies to use :hugs: Transformers with the PyTorch Lightning Trainer.

Quick Recipes

Train bert-base-cased on the CARER emotion dataset using the Text Classification task.

import pytorch_lightning as pl
from transformers import AutoTokenizer

from lightning_transformers.task.nlp.text_classification import (
    TextClassificationDataModule,
    TextClassificationTransformer,
)

tokenizer = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path="bert-base-cased"
)
dm = TextClassificationDataModule(
    batch_size=1,
    dataset_name="emotion",
    max_length=512,
    tokenizer=tokenizer,
)
model = TextClassificationTransformer(
    pretrained_model_name_or_path="bert-base-cased", num_labels=dm.num_classes
)

trainer = pl.Trainer(accelerator="auto", devices="auto", max_epochs=1)

trainer.fit(model, dm)

Train a pre-trained mt5-base backbone on the WMT16 dataset using the Translation task.

import pytorch_lightning as pl
from transformers import AutoTokenizer

from lightning_transformers.task.nlp.translation import (
    TranslationTransformer,
    WMT16TranslationDataModule,
)

tokenizer = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path="google/mt5-base"
)
model = TranslationTransformer(
    pretrained_model_name_or_path="google/mt5-base",
    n_gram=4,
    smooth=False,
    val_target_max_length=142,
    num_beams=None,
    compute_generate_metrics=True,
)
dm = WMT16TranslationDataModule(
    # WMT translation datasets: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']
    dataset_config_name="ro-en",
    source_language="en",
    target_language="ro",
    max_source_length=128,
    max_target_length=128,
    padding="max_length",
    tokenizer=tokenizer,
)
trainer = pl.Trainer(accelerator="auto", devices="auto", max_epochs=1)

trainer.fit(model, dm)

Lightning Transformers supports a bunch of :hugs: tasks and datasets. See the documentation.

Billion Parameter Model Support

Big Model Inference

It's really easy to enable large model support for the pre-built LightningModule :hugs: tasks.

Below is an example to enable automatic model partitioning (across CPU/GPU and even leveraging disk space) to run text generation using a 6B parameter model.

import torch
from accelerate import init_empty_weights
from transformers import AutoTokenizer

from lightning_transformers.task.nlp.language_modeling import (
    LanguageModelingTransformer,
)

with init_empty_weights():
    model = LanguageModelingTransformer(
        pretrained_model_name_or_path="EleutherAI/gpt-j-6B",
        tokenizer=AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"),
        low_cpu_mem_usage=True,
        device_map="auto",  # automatically partitions the model based on the available hardware.
    )

output = model.generate("Hello, my name is", device=torch.device("cuda"))
print(model.tokenizer.decode(output[0].tolist()))

For more information see Big Transformers Model Inference.

Big Model Training with DeepSpeed

Below is an example of how you can train a 6B parameter transformer model using Lightning Transformers and DeepSpeed.

import pytorch_lightning as pl
from transformers import AutoTokenizer

from lightning_transformers.task.nlp.language_modeling import (
    LanguageModelingDataModule,
    LanguageModelingTransformer,
)

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="gpt2")

model = LanguageModelingTransformer(
    pretrained_model_name_or_path="EleutherAI/gpt-j-6B",
    tokenizer=AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"),
    deepspeed_sharding=True,  # defer initialization of the model to shard/load pre-train weights
)

dm = LanguageModelingDataModule(
    batch_size=1,
    dataset_name="wikitext",
    dataset_config_name="wikitext-2-raw-v1",
    tokenizer=tokenizer,
)
trainer = pl.Trainer(
    accelerator="gpu",
    devices="auto",
    strategy="deepspeed_stage_3",
    precision=16,
    max_epochs=1,
)

trainer.fit(model, dm)

For more information see DeepSpeed Training with Big Transformers Models or the Model Parallelism documentation.

Contribute

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Community

For help or questions, join our huge community on Slack!