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μP for Tensorflow

This is a Tensorflow 2 (very preliminary) port of Yang and Hu et al.'s μP repo

Installation

To install, you can either clone the repo and install the package locally, or install it from pyPI.

pip install mup-tf

Install from Source

git clone https://github.com/zanussbaum/mup-tf.git
pip install -e .

Basic Usage

This has been adapted from the original MuP repo.

import tensorflow as tf
from mup_tf import MuReadout, make_base_shapes, set_base_shapes, MuSGD, MuAdam

class MyModel(tf.keras.Model):
    def __init__(self, width, ...):
        ...
        ### In model definition, replace output layer with MuReadout
        # readout = tf.keras.layers.Dense(d_out)
        readout = MuReadout(d_out)
        ### If tying weights with an input Embedding layer, do
        # readout = MuSharedReadout(input_layer.weight)
        ...
    def call(self, ...):
        ...
        ### If using a transformer, make sure to use
        ###   1/d instead of 1/sqrt(d) attention scaling
        # attention_scores = query @ key.T / d**0.5
        attention_scores = query @ key.T * 8 / d
        ### We use 8/d instead of 1/d here to be backward compatible
        ###   with 1/d**0.5 when d=64, a common head dimension.
        ...

### Instantiate a base model
base_model = MyModel(width=1)
### Instantiate a "delta" model that differs from the base model
###   in all dimensions ("widths") that one wishes to scale.
### Here it's simple, but e.g., in a Transformer, you may want to scale
###   both nhead and dhead, so the delta model should differ in both.
delta_model = MyModel(width=2) 

### Instantiate the target model (the model you actually want to train).
### This should be the same as the base model except 
###   the widths could be potentially different.
### In particular, base_model and model should have the same depth.
model = MyModel(width=100)

### Set base shapes
### When `model` has same parameter shapes as `base_model`,
###   `model` behaves exactly the same as `base_model`
###   (which is in Tensorflow's default parametrization).
###   This provides backward compatibility at this particular model size.
###   Otherwise, `model`'s init and LR are scaled by μP.
### IMPORTANT: this should be called as soon as possible,
###   before re-initialization and optimizer definition.
infshapes = set_base_shapes(model, base_model, delta=delta_model)

### Alternatively, one can save the base model shapes in a file
# make_base_shapes(base_model, delta_model, filename)
### and later set base shapes directly from the filename
# set_base_shapes(model, filename)
### This is useful when one cannot fit both 
###   base_model and model in memory at the same time

### Replace your custom init, if any
for param in model.parameters():
    ### If initializing manually with fixed std or bounds,
    ### then replace with same function from mup.init
    # torch.nn.init.uniform_(param, -0.1, 0.1)
    mup.init.uniform_(param, -0.1, 0.1)
    ### Likewise, if using
    ###   `xavier_uniform_, xavier_normal_, kaiming_uniform_, kaiming_normal_`
    ### from `torch.nn.init`, replace with the same functions from `mup.init`

### Use the optimizers from `mup.optim` instead of `tf.keras.optimizers`
# optimizer = tf.keras.optimizers.SGD(learning_rate=0.1)
opt_kwargs = {"infshapes": name2shapes}
# need to pass in infshapes to optimizer if you are using tf.distribute.MirrorStrategy
# as tensors are reset and the `infshape` attribute is lost
optimizer = MuSGD(learning_rate=0.1 **opt_kwargs)