Crazy Fast: Pre-train our custom 123M parameter LLM to ~3.5 validation loss against The Pile in just twenty minutes on a consumer grade GeForce RTX™ 4090
Batteries included: Comes with components and full config file setups for state of the art models like RWKV5, LLaMa2, and Hyena
No Setup: Automatically stream your dataset from the web, so there's no setup or downloading required
Research or Learn: Great for seasoned professionals as well as people new to Machine Learning
Clean Code: Best practices and extensible, clearly written, self-documenting code let you focus on your work
- Create, train, and run new LLMs
- Easily compare pre-training results for pre-existing SoTA models like RWKV5, LLaMa2, Hyena or variants
- Put together new models from easy to use components
- Simple Python-syntax model configuration file
- Create new modules and easily run experiments to see which work best
- Read the source code for our custom GPT Alpha LLM to learn top techniques
If you want to compare training today's open source LLMs, invent new ones, do experiments, or just get a clean framework so you don't have to invent one, this repo might be for you.
pip install torch lightning deepspeed einops transformers datasets wandb
If you want to just get started quickly, there are lots of pre-existing examples of config files to look at or try in the configs/ directory.
GPT Core config files are used to set hyperparameters as well as select the components that make up your model. They follow Python syntax, and FULLY SUPPORT AUTOCOMPLETE in editors like VSCode. This makes it very easy to edit them, check them for errors, or see what options are available - all from within your favorite IDE.
Example config file:
import cli
import model.core
import lit
import torch.optim
MAX_SEQUENCE_LENGTH = 1024
cli.Config(
seed_everything = 1337,
compile = True,
model_factory = lambda: model.core.Decoder(
max_sequence_length=MAX_SEQUENCE_LENGTH,
),
trainer_factory = lambda: lit.CoreLightningTrainer(
optimizer_factory = lambda: torch.optim.Adam(lr=6e-4),
),
)GPT Core config files contain imports, then optional variable assignments, then a final expression that is considered the result of the config file.
These files are just a way of constructing an object like you would in Python. The best reference for what options a configuration object takes is the source code itself. The main outer config definition can be found at the top of cli.py - there you will see @dataclass class Config: Other classes instantiated within your config file just take whatever arguments their constructors normally would.
The only special thing to know about GPT Core config files is that any parameter that ends in _factory should be set via a lambda:
Example: train_dataloader_factory = lambda: torch.utils.data.DataLoader()
This is because components require deferred instantiation. Deferral of instantiation is done with a class called util.config.Factory, which strongly resembles a Python's partial used for partial function invocation. Within a config file, if you specify lambda: before you call a function or class constructor, it will automatically wrap your function or class constructor in a Factory. This is very similar to how lambda: acts in Python already.
python cli.py train -c configs/gptalpha.cfg.pyGPT Core currently supports the Lightning trainer via its class lit.CoreLightningTrainer. The GPT Core class lit.CoreLightningTrainer exactly matches the Lightning API, slightly flattened for ease of use in config files. So as you explore the autocomplete for CoreLightningTrainer you can look at the lightning docs to learn how to do various tasks like continue from a checkpoint, add custom callbacks during training, etc.
Consider trying other huggingface datasets, such as OpenWebText:
train_dataset = datasets.load_dataset(path='Skylion007/openwebtext', streaming=True, split='train')
The example config files show how to use WandB for logging, but any logger, multiple loggers, or even no logger can be used. See lightning docs for more info.
You can easily choose which metrics to log using the config system and parameterize them like so (or create new ones):
metric_factories=dict(
loss=lambda: metrics.Loss(),
acc=lambda: metrics.Accuracy()
),Setting compile = True causes your model to be compiled using torch.compile(model) This can dramatically improve training speeds.
It can also cause very slow startup times and even break, if you use features in your model that are unsupported by dynamo. (for example, the complex torch datatype)
To have a validation done and checkpoint written, add the following config setting to trainer:
trainer_factory = lambda: lightning.Trainer(
val_check_interval=1024, # choose whatever number of steps you'd like here
)Continue a training run from an existing checkpoint by supplying the following kind of config setting:
trainer_factory = lambda: lit.CoreLightningTrainer(
ckpt_path='checkpoints/epoch=0-step=256.ckpt',
)python cli.py eval -c configs/gptalpha.cfg.pyInference is not the main mission of GPT Core, and will be updated to be more flexible and useful in a future release.
GPT Alpha is the custom LLM that comes with GPT Core. It employs many of the best components found in state of the art LLM research, as well as our own experiments to achieve fast training to low perplexity and loss.
The following are some of the improvements it uses:
Small embedding initializations citation
Embeddings are initialized to a small value but are then immediately normalized. This immediately creates an embedding space that is well distributed around the unit hypersphere, and converges in a rapid fashion with desirable qualities, even with no warmup.
Weight Tying citation
The unembedding which translates from the final layer embeddings back to token ids relies on the same weights as the embedding, resulting in faster training and significantly smaller model size.
Attention with Linear Biases (ALiBi) citation
We use ALiBi to bias attention results, so that a flexible form of positional information is used by the network.
Prior formulations used BatchNorm or LayerNorm, but with proper initialization and scaling we can use root mean square norm and sometimes unscaled L2 norm with no weight learning. This results in faster learning with no downsides.
Attention Sublayer Gating citation
A learned gate is added to the final output of the attention sublayer.
Query, Key, and Value Normalization self-citation
We normalize query, keys, and values prior to the computation of attention.
Independent Value Head Size citation
We allow values to be larger than keys and queries. This lets the network "think harder" about the embeddings it attends to, with only a small loss in training speed.
Attention Group Normalization citation
We normalize each head of the attention computation output separately using RMSNorm to form a kind of group norm by head.
Time Lerp citation
Instead of using the token embedding at a specific sequence position, we use a mix of it with the sequentially prior embedding. This costs very little but dramatically improves network performance.
We adopt most of the practices from the RWKV channel mix feed forward network. This includes Time Lerp, which is used in our feed forward network as well as other places, and specialized gating.
Residual Mixing self-citation
Sublayers (attention and feed forward) are mixed with the residual using a learned vector ratio L via L*residual+(2-L)*value. We find this has slightly better results and costs very little to evaluate.
No bias citation
Linear layers contain only weights without biases throughout the model
Sequence Packing citation
Sequences in the training data that do not reach the end of the sequence length buffer are packed together instead of padded to the end, so that every token examined is useful for learning instead of large numbers of padding tokens becoming a waste of compute.
- colab notebook so anyone can easily try GPT Core
- documentation on components
- documentation on dataset collation etc.
- improved weight initialization mechanisms
- true support for recurrent state
- encoder-decoder models and components (maybe T5?)
- some wandb charts of actual training runs
- clear and easy mechanisms for distributed training
- self-implemented modular components that match parts of third party models like retnet, rwkv5
- improved inference
- allow separation of config for model, logging, dataset
- testing apparatus (BLEU score etc.)
- Mixture of Experts
- MixCE