Basis Lab

Reading the Paper That Started It

Reading the Results Tables With the Cost Column In

Last timeHow It Was Actually Trained

The results table in the 2017 transformer paper has a training cost column, and rebuilding it from the paper's own footnote changes which row wins.

The results section of the paper is two tables and about a page of text, and the

two tables are quoted far more often than they are read. The first has five

columns and almost every summary of the paper quotes one of them. The second has

twelve and almost no summary quotes any.

This lesson rebuilds both with the arithmetic in view. The point is not that the

paper is wrong anywhere. It is that the tables support fewer claims than they are

routinely made to support, and the missing claims are findable by hand.

The column most readers skip

The results table lists ten systems with their scores on two translation tasks,

English to German and English to French, and then gives two more columns headed

training cost in floating point operations. The footnote says how those were

produced. The paper multiplies the training time by the number of devices used

by an estimate of the sustained single precision capacity of each device, and it

states the capacities it assumed, which are 2.8, 3.7, 6.0 and 9.5 teraflops for

the K80, K40, M40 and P100 respectively. One teraflop per second is a million

million floating point operations per second.

Below, the whole table, with the costs written in units of ten to the eighteenth

operations so that they can be compared by eye. One unit is a billion billion

multiply-and-add style operations, and the smallest entry in the table is 3.3 of

them. The single models come first.

FIG 1
systemEN-DE scoreEN-FR scoreEN-DE costEN-FR cost
ByteNet23.75not listednot listednot listed
Deep-Att with PosUnknot listed39.2not listed100
GNMT with RL24.639.9223140
ConvS2S25.1640.469.6150
MoE26.0340.5620120
Transformer, base27.338.13.33.3
Transformer, big28.441.82323

The two transformer rows carry one cost each because the same configuration was

trained for each language pair, so the single figure spans both columns in the

original.

Then the ensembles, which are several trained copies of one system voting

together. They are in the same table in the paper and they are a different kind

of entry: the score is bought by paying for the whole set, which is why their

cost column runs in the hundreds.

The lesson stops here

9 more paragraphs to go

You have read the opening. The rest of the argument, the problems that check whether it landed, and the lines worth keeping at the end all come with a plan.

The first lesson of every course in the library reads the whole way through, free, so you can see exactly what the rest of them are.

See the planThe contents

This is the reading half

Starting the course gives you your own copy of it. Every idea on every page has problems standing under it, marked with a reason rather than a tick, and any sentence you do not believe can be opened and argued with. None of that can happen on a page nobody owns.

The contents

The rest of this course

  1. 01Why Attention Replaced Recurrence When It Costs More
  2. 02What the Attention Equation Actually Computesopening only
  3. 03Why Eight Attention Heads and Not One Wide Oneopening only
  4. 04Why the Position Signal Is a Ladder of Sine Wavesopening only
  5. 05Most of the Block Is Not Attentionopening only
  6. 06The Training Recipe, Read as a List of Repairsopening only
  7. 07Reading the Results Tables With the Cost Column Inyou are here
  8. 08Sorting the Paper's Choices Into Three Pilesopening only

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