Optimum documentation

Optimization

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# Optimization

The optimum.fx.optimization module provides a set of torch.fx graph transformations, along with classes and functions to write your own transformations and compose them.

## The transformation guide

In 🤗 Optimum, there are two kinds of transformations: reversible and non-reversible transformations.

### Write a non-reversible transformation

The most basic case of transformations is non-reversible transformations. Those transformations cannot be reversed, meaning that after applying them to a graph module, there is no way to get the original model back. To implement such transformations in 🤗 Optimum, it is very easy: you just need to subclass Transformation and implement the transform() method.

For instance, the following transformation changes all the multiplications to additions:

>>> import operator
>>> from optimum.fx.optimization import Transformation

...     def transform(self, graph_module):
...         for node in graph_module.graph.nodes:
...             if node.op == "call_function" and node.target == operator.mul:
...         return graph_module

After implementing it, your transformation can be used as a regular function:

>>> from transformers import BertModel
>>> from transformers.utils.fx import symbolic_trace

>>> model = BertModel.from_pretrained("bert-base-uncased")
>>> traced = symbolic_trace(
...     model,
... )

>>> transformed_model = transformation(traced)

### Write a reversible transformation

A reversible transformation implements both the transformation and its reverse, allowing to retrieve the original model from the transformed one. To implement such transformation, you need to subclass ReversibleTransformation and implement the transform() and reverse() methods.

For instance, the following transformation is reversible:

>>> import operator
>>> from optimum.fx.optimization import ReversibleTransformation

>>> class MulToMulTimesTwo(ReversibleTransformation):
...     def transform(self, graph_module):
...         for node in graph_module.graph.nodes:
...             if node.op == "call_function" and node.target == operator.mul:
...                 x, y = node.args
...                 node.args = (2 * x, y)
...         return graph_module
...
...     def reverse(self, graph_module):
...         for node in graph_module.graph.nodes:
...             if node.op == "call_function" and node.target == operator.mul:
...                 x, y = node.args
...                 node.args = (x / 2, y)
...         return graph_module

### Composing transformations together

As applying multiple transformations in chain is needed more often that not, compose() is provided. It is an utility function that allows you to create a transformation by chaining multiple other transformations.

>>> from optimum.fx.optimization import compose
>>> composition = compose(MulToMulTimesTwo(), ChangeMulToAdd())