This is a JAX transform which implements LoRA: Low-Rank Adaptation of Large Language Models. LoRA replaces operations like Wx with (W + BA)x where A and B are skinny rectangular matrices. You can then train only A and B, and leave W frozen, which dramatically reduces the amount of memory needed for things like optimizer states.
Lorax should work on most JAX models. I did my testing with my models which use Haiku, and you can find an example of applying it to a HuggingFace Flax model in the [examples directory(examples/).
pip install jax-lorax- Replaced backend with Qax
- Overhauled API to simplify usage (No more need to separately handle frozen/tunable params)
Install dev dependencies:
git clone https://github.com/davisyoshida/lorax.git
cd lorax
pip install poetry
poetry installRun tests:
pytest tests.py
Lorax makes it so you can take model code which wasn't written with LoRA in mind, and transform it so that it does! For example, consider the following MLP code:
import jax
import jax.numpy as jnp
import optax
def model(params, x):
"""My model, written in the dark ages before LoRA, using gratuitous amounts of VRAM when trained"""
for massive_w in params:
x = jax.nn.relu(x @ massive_w)
return jnp.sum(x)
dim = 5000
# Initialize about 3 GB of params
params = [jax.random.normal(jax.random.PRNGKey(i), (dim, dim)) / (dim ** 0.5) for i in range(30)]
optimizer = optax.adam(learning_rate=3e-4)
# OOM on 7GB GPU :(
opt_state = optimizer.init(params)The optimizer states are way too expensive, but applying Lorax lets you just train two 5000 x 64 matrices for each original weight.
First import lorax and transform your model:
import lorax
# Transform the model code
lora_model = lorax.lora(model)Next initialize the new LoRA parameters:
# Tell LoRA what to use as the small dimension of B and A
rank_constraint = 64
lora_spec = [rank_constraint for param in params]
# Initialize a set of LoRA factors for each parameter
lora_params = lorax.init_lora(param_tree=params, spec=lora_spec, rng=jax.random.PRNGKey(0))
# The transformed model has the same call signature, but it can now handle parameters
# of type lorax.LoraWeight
lora_model(lora_params, jnp.ones((dim,)))
# Wrap the optimizer so it will freeze parameters not marked as trainable by the spec
optimizer = lorax.wrap_optimizer(optimizer, lora_spec)
# Now the optimizer can be used just like normal
opt_state = optimizer.init(lora_params)That's it for the Lorax specific stuff. The wrapped lora_model function is just an ordinary
JAX function, and the LoraWeight instances a pytrees.
# Normal update function:
@jax.jit
def update_fn(lora_params, opt_state, x):
grad_fn = jax.value_and_grad(lora_model)
loss, grad = grad_fn(lora_params, x)
updates, new_opt_state = optimizer.update(grad, opt_state, params=lora_params)
updated_params = optax.apply_updates(lora_params, updates)
return loss, new_opt_state, updated_paramsNow for some dummy data and the training loop:
x = jax.random.normal(jax.random.PRNGKey(0), (dim,))
for i in range(10):
loss, opt_state, lora_params = update_fn(lora_params, opt_state, x)
print(f'Step: {i} loss: {loss:.4e}') # Number goes down!
# Step: 0 loss: 6.6614e-02
# Step: 1 loss: 4.4402e-02
# Step: 2 loss: 3.0241e-02
# Step: 3 loss: 1.8457e-02
# Step: 4 loss: 1.2326e-02
# Step: 5 loss: 8.8878e-03
# Step: 6 loss: 6.0599e-03
# Step: 7 loss: 4.3899e-03
# Step: 8 loss: 3.0839e-03
# Step: 9 loss: 2.2423e-03Number goes down! We can now merge the trained LoRA params with the frozen params, and use them with the unmodified model:
lora_output = lora_model((frozen_params, tunable_params), x)
# Now we merge the params to get params usable in the original model
merged_params = lorax.merge_params(lora_params)
orig_model_output = model(merged_params, x)
# Verify that the model outputs are the same
print(f'Difference between split and merged outputs: {orig_model_output - lora_output:.3e}')
# Difference between split and merged params: 1.164e-10See examples/huggingface_gpt2.py for an example applying Lorax to a realistic model.
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