Hackernoon logoThe Facebook TransCoder Explained: Converting Coding Languages with AI by@whatsai

The Facebook TransCoder Explained: Converting Coding Languages with AI

Louis Bouchard Hacker Noon profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

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!

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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

Video transcript

00:00

this new model converts code from a

00:02

programming language to another without

00:04

any supervision

00:05

it can take a python function and

00:07

translate it into a c

00:08

function and vice versa without any

00:10

prior examples

00:12

it understands the syntax of each

00:14

language and can thus generalize

00:16

to any programming languages let's see

00:18

how they did that

00:21

[Music]

00:25

this is what's ai and i share artificial

00:27

intelligence news every week

00:29

if you are new to the channel and want

00:31

to stay up to date please consider

00:32

subscribing to not miss

00:34

any further news to understand this new

00:36

model by facebook

00:38

called the transcoder we first need to

00:40

introduce what it is

00:41

in short it's a trans compiler meaning

00:44

that it is a source to source translator

00:46

which converts a source code from a high

00:48

level programming language

00:50

such as c plus or python to another one

00:54

currently this type of translation

00:55

requires manual modifications

00:58

in order to work properly because of the

01:00

complexity of the task

01:01

it is hard to respect the target

01:03

language conventions

01:04

when changing from a programming

01:06

language a to a programming language b

01:09

plus since it is a complex task it

01:12

requires a lot of knowledge

01:13

in both target and source programming

01:15

languages to work

01:16

which means a lot of examples and

01:19

computation time

01:20

this is why facebook tried this new

01:22

approach using unsupervised machine

01:24

translation to train this fully

01:26

unsupervised

01:27

neural trans compiler the model was

01:29

trained on open source github projects

01:32

and mainly trained to translate

01:33

functions between three programming

01:35

languages

01:36

c plus plus java and python they

01:38

achieved that using a sequence to

01:40

sequence model with attention

01:42

composed of an encoder and a decoder

01:44

with a transformer architecture

01:46

trained in an unsupervised way

01:48

specifically on functions

01:50

at first they initializes the model with

01:53

cross-lingual mask language

01:55

model pre-training this is done by

01:58

randomly masking

01:59

some of the tokens and training the

02:01

transcoder to predict these mask tokens

02:04

based on the context allowing the model

02:06

to create high quality

02:08

sequence representations regardless of

02:10

the programming language

02:12

once the pre-training is done the

02:13

decoder is then trained

02:15

using the pre-trained transcoder as

02:17

input to always generate

02:19

valid code sequences even when fed with

02:21

noisy data

02:22

increasing the encoder robustness to

02:24

input noise

02:26

in order to translate functions from one

02:28

language to another

02:29

we need to add this last part the back

02:31

translation

02:33

which is composed of two models as you

02:35

can see in this image

02:36

the target to source model which is used

02:39

to translate the target code into the

02:41

source code

02:42

producing a noisy source sequence then

02:45

this sequence is used in the source to

02:47

target model to reconstruct the target

02:49

sequence

02:50

from this noisy input if this doesn't

02:53

tell you anything

02:54

i invite you to check out the videos i

02:56

made on attention

02:57

and transformers which are linked in the

02:59

description below

03:01

as the results show there are still a

03:03

lot of progress to be done in the field

03:05

but the best translation shows over 90

03:08

success

03:08

which is a huge jump in accuracy here

03:10

are some examples of functions

03:12

translated using the transcoder

03:15

i invite you to pause the video and take

03:17

a deeper look at these examples

03:35

programmers will be able to easily

03:37

improve and build

03:38

robust cross-platform software with

03:41

these improvements in translations

03:43

with little or no modifications to make

03:45

to their code

03:46

using a single code base the code and

03:49

pre-training models will be publicly

03:51

available soon as they said in the paper

03:54

of course this was just a simple

03:56

overview of this unsupervised code

03:58

translator

03:58

i strongly recommend to read the paper

04:00

link to the description to learn more

04:02

about it

04:02

and try out the code whenever it's

04:04

available leave a like if you went this

04:06

far in the video

04:08

and since there are over 90 of you guys

04:10

watching that are not subscribed yet

04:12

please consider subscribing to the

04:14

channel to not miss any further news

04:16

clearly explained if you want to support

04:18

the channel i now have a patreon

04:20

linked in the description where you can



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