The Facebook TransCoder Explained: Converting Coding Languages with AI

This new model converts code from a programming language to another without any supervision. It can take a Python function and translate it into a C++ function, and vice-versa, without any prior examples. It understands the syntax of each language and can thus generalize to any programming language!

This week my interest was directed towards TransCoder. A new model by the AI team at Facebook Research. Ask any questions or remarks you have in the comments, I will gladly answer everything!

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The TransCoder Paper: https://arxiv.org/abs/2006.03511

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this new model converts code from a

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programming language to another without

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any supervision

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it can take a python function and

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translate it into a c

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function and vice versa without any

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prior examples

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it understands the syntax of each

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language and can thus generalize

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to any programming languages let's see

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how they did that

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any further news to understand this new

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model by facebook

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called the transcoder we first need to

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introduce what it is

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in short it's a trans compiler meaning

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that it is a source to source translator

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which converts a source code from a high

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level programming language

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such as c plus or python to another one

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currently this type of translation

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requires manual modifications

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in order to work properly because of the

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complexity of the task

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it is hard to respect the target

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language conventions

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when changing from a programming

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language a to a programming language b

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plus since it is a complex task it

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requires a lot of knowledge

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in both target and source programming

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languages to work

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which means a lot of examples and

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computation time

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this is why facebook tried this new

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approach using unsupervised machine

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translation to train this fully

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unsupervised

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neural trans compiler the model was

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trained on open source github projects

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and mainly trained to translate

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functions between three programming

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languages

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c plus plus java and python they

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achieved that using a sequence to

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sequence model with attention

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composed of an encoder and a decoder

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with a transformer architecture

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trained in an unsupervised way

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specifically on functions

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at first they initializes the model with

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cross-lingual mask language

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model pre-training this is done by

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randomly masking

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some of the tokens and training the

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transcoder to predict these mask tokens

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based on the context allowing the model

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to create high quality

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sequence representations regardless of

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the programming language

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once the pre-training is done the

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decoder is then trained

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using the pre-trained transcoder as

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input to always generate

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valid code sequences even when fed with

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noisy data

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increasing the encoder robustness to

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input noise

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in order to translate functions from one

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language to another

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we need to add this last part the back

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translation

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which is composed of two models as you

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can see in this image

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the target to source model which is used

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to translate the target code into the

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source code

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producing a noisy source sequence then

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this sequence is used in the source to

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target model to reconstruct the target

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sequence

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from this noisy input if this doesn't

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tell you anything

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i invite you to check out the videos i

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made on attention

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and transformers which are linked in the

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description below

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as the results show there are still a

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lot of progress to be done in the field

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but the best translation shows over 90

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success

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which is a huge jump in accuracy here

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are some examples of functions

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translated using the transcoder

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i invite you to pause the video and take

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a deeper look at these examples

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programmers will be able to easily

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improve and build

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robust cross-platform software with

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these improvements in translations

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with little or no modifications to make

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to their code

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using a single code base the code and

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pre-training models will be publicly

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available soon as they said in the paper

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of course this was just a simple

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overview of this unsupervised code

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translator

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i strongly recommend to read the paper

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link to the description to learn more

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about it

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and try out the code whenever it's

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available leave a like if you went this

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far in the video

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