Understand and test language model architectures on synthetic tasks.
Zoology provides machine learning researchers with a simple playground for understanding and testing language model architectures on synthetic tasks. This repository can be used to reproduce the results in our paper Zoology: Measuring and Improving Recall in Efficient Language Models.
Why did we make Zoology? In our research on efficient language models, synthetic tasks have been crucial for understanding and debugging issues before scaling up to expensive pretraining runs. So, we're releasing the code we've used alongside instructions for replicating a lot of our experiments and their WandB logs. Simplicity is our main design goal: limited dependencies, architecture implementations that are easy to understand, and a straightforward process for adding new synthetic tasks.
Is Zoology a good fit for your use case? If you are looking to actually train a large machine learning model, Zoology's training harness (which is optimized for simplicity) is certainly not a good fit. For our language model research, we've found the GPT-NeoX useful for this. That being said, you might still want to use some of Zoology's layer implementations or maybe even mix the synthetic tasks into your training distribution.
I want to explore the Based architecture. How should I get started? Follow along the Getting Started and Configuration settings. Once you know how to launch a sweep, we can turn our attention to this experiment file. You can toggle the sequence mixers here to launch synthetic experiments with Based. Recall that Based is an architecture with a gated convolution layer and a linear attention layer. You can see here how we will be initializing a 2-layer model, one layer of each type, for training in these experiments. More to come for Based soon!
Installation. First, ensure you have torch installed, or install it following the instructions here. Then, install Zoology with:
git clone https://github.com/HazyResearch/zoology.git
cd zoology
pip install -e .[extra,analysis] If you want to keep this install as lightweight as possible; the only required dependencies are: torch, einops, tqdm, pydantic, wandb. There is some extra functionality (e.g. launching sweeps in parallel with Ray) that require additional dependencies. To install without the optional dependencies, run pip install -e ..
Then, try running an example experiments with:
python -m zoology.launch zoology/experiments/examples/basic.py
This will train a simple two layer transformer on multi-query associative recall. To run a sweep over learning rates, try:
python -m zoology.launch zoology/experiments/examples/basic_sweep.py
If you have access to multiple GPUs, you can run the sweep in parallel by adding the -p flag.
In this section, we'll walk through how to configure an experiment and launch sweeps.
Configuration. Models, data, and training are controlled by configuration objects. For details on available configuration fields, see the configuration definition in zoology/config.py. The configuration is a nested Pydantic model, which can be instantiated as follows:
from zoology.config import TrainConfig, ModelConfig, DataConfig, ModuleConfig, FunctionConfig
config = TrainConfig(
max_epochs=20,
data=DataConfig(
vocab_size=128,
builder=FunctionConfig(
name="zoology.data.associative_recall.gap_power_distr_ar",
kwargs={"num_kv_pairs": 4}
),
),
model=ModelConfig(
vocab_size=128,
sequence_mixer=ModuleConfig("name": "zoology.mixers.attention.MHA"}
),
)Note that the FunctionConfig and ModuleConfig are special objects that configure partial functions and PyTorch modules, respectively.
They both have an instantiate() method that will import the function or class passed to name and partial or instantiate it with kwargs.
For example,
fn_config = FunctionConfig(name="torch.sort", kwargs={"descending": True})
fn = fn_config.instantiate()
fn(torch.tensor([2,4,3])) # [4, 3, 2]Launching experiments. To launch an experiment from the command line, define a configuration object in python file and store it in a global variable configs:
config = TrainConfig(...)
configs = [config]See zoology/experiments/examples/basic.py for an example.
Then run python -m zoology.launch zoology/experiments/examples/basic.py, replacing basic.py with the path to your experiment. This will launch a single training job.
Launching sweeps. To launch a sweep, simply add more configuration objects to the configs list. For example, here's the content of zoology/experiments/examples/basic_sweep.py:
import numpy as np
from zoology.config import TrainConfig
configs = []
for lr in np.logspace(-4, -2, 10):
configs.append(TrainConfig(learning_rate=lr)) You can then run python -m zoology.launch zoology/experiments/examples/basic_sweep.py. This will launch a sweep with 10 jobs, one for each configuration.
Launching sweeps in parallel. If you have multiple GPUs on your machine, you can launch sweeps in parallel across your devices.
To launch sweeps in parallel, you'll need to install Ray: pip install -e.[extras].
Then, you can run python -m zoology.launch zoology/experiments/basic_sweep.py -p.
This will run the configurations in parallel using a pool of workers, one per GPU.
Logging. Zoology uses Weights and Biases for logging. You'll need to login with wandb login and update the LoggerConfig in your configuration to point to your project:
from zoology.config import TrainConfig, LoggerConfig
TrainConfig(
logger=LoggerConfig(
project="my_wandb_project",
entity="my_wandb_entity",
),
...
)In this section, we'll walk through how to create a new synthetic task and discuss some of the tasks that are already implemented.
Creating a new task. To create a new task, you'll need to implement a data builder function with the following signature:
from zoology.utils.data import SyntheticData
def my_data_builder(
num_train_examples: int,
num_test_examples: int,
vocab_size: int,
input_seq_len: int,
seed: int,
**kwargs
) -> SyntheticData:
...You can add any additional arguments to the function signature, but the ones above are required.
The function must return a SyntheticData object, which is a simple dataclass with the following fields:
@dataclass
class SyntheticData:
"""Simple dataclass which specifies the format that should be returned by
the synthetic data generators.
Args:
train_inputs (torch.Tensor): Training inputs of shape (num_train_examples, input_seq_len)
train_labels (torch.Tensor): Training labels of shape (num_train_examples, input_seq_len)
test_inputs (torch.Tensor): Test inputs of shape (num_test_examples, input_seq_len)
test_labels (torch.Tensor): Test labels of shape (num_test_examples, input_seq_len)
"""
train_inputs: torch.Tensor
train_labels: torch.Tensor
test_inputs: torch.Tensor
test_labels: torch.TensorThe inputs and labels should be integer tensors with values in the range [0, vocab_size).
You can create this function in any file you want, as long as it's importable. Let's
assume that we've created a file zoology/data/my_task.py and written our my_data_builder function there.
Then, we can add it to our data configuration with:
from zoology.config import TrainConfig, DataConfig, FunctionConfig
config = TrainConfig(
DataConfig(
vocab_size=128,
builder=FunctionConfig(name="zoology.data.my_task.my_data_builder"),
kwargs={"my_data_builder_kwarg": 4}
),
),
)When you launch an experiment with this configuration, the my_data_builder function will be imported and called with the specified arguments, constructing the dataset.
Caching dataset creation. Sometimes it's useful to cache the dataset creation process, especially if it's expensive. To do so you can pass a cache_dir to the DataConfig: DataConfig(..., cache_dir="my_cache_dir").
This repo is being developed by members of the HazyResearch group.
If you use this codebase, or otherwise found our work valuable, please cite:
@article{zoology2023,
title={Zoology: Measuring and Improving Recall in Efficient Language Models},
author={Arora, Simran and Eyuboglu, Sabri and Timalsina, Aman and Johnson, Isys and Poli, Michael and Zou, James and Rudra, Atri and Ré, Christopher},
journal={ arXiv:2312.04927},
year={2023}
}