This repo implements muP (paper) for selected PyTorch models in Huggingface Transformers. The primary purpose of this repo is as a clean demonstration of how to inject muP into different variants of transformers. As a secondary purpose, one can also use the models here as provided.

Go to this project directory and do

pip install -r requirements.txt
pip install -e .

Taking BERT as an example, there are two files modeling_bert.py and configuration_bert.py in mutransformers/models/bert/ we copied from Huggingface Transformers and made a small number of modifications to implement muP. Our modifications in these files can all be found by searching for ### muP.

These files are copied from Huggingface Transformers v4.16.2. We provide the original files as _original_*.py for easy comparison, for example, _original_modeling_bert.py.

Coordinate checking is a way of verifying that muP is implemented correctly just like gradient checking is a way of verifying that autograd is implemented correctly. You can find the coord check results in tests/coordcheck/CoordCheck.ipynb. You can rerun the notebook yourself as well after installation.

For example, the coord check for BERT in standard parametrization (SP) shows many activations blow up with width, but the same for BERT in muP shows activation scale consistent with width.

The models here can be used for your training purposes as well, though we have not made sure to replicate the original numbers of each of these transformer models. The models in this package can be used as follows, taking BERT as an example:

from mutransformers import BertConfig, BertForMaskedLM
from mup import make_base_shapes, set_base_shapes, MuAdamW
from functools import partial
# define a base model
base_config = BertConfig(
    hidden_size=256,
    intermediate_size=256,
    num_attention_heads=16,
)
base_model = BertForMaskedLM(config=base_config)
# define a delta models where we vary all "widths" we want to vary
delta_config = BertConfig(
    hidden_size=200,
    intermediate_size=300,
    num_attention_heads=5,
)
delta_model = BertForMaskedLM(config=delta_config)
# define a base shape object based on comparing delta_model against base_model
base_shapes = make_base_shapes(base_model, delta_model, savefile='bert256.bsh')

# define target model
target_config = BertConfig(
    hidden_size=1024,
    intermediate_size=1024*4,
    num_attention_heads=32,
)
target_model = BertForMaskedLM(config=target_config)

# set base shapes
set_base_shapes(target_model, base_shapes)
# you can alternatively load base shape from file
# set_base_shapes(target_model, 'bert256.bsh')

# re-initialize
target_model.apply(target_model._init_weights)

# make sure to use mup optimizers for training
optimizer = MuAdamW(target_model.parameters(), lr=1e-3)

# train
...

For more general information on how to use mup, see the muP package documentation.

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