AdditiveQuantizer `max_beam_size` in training

[guillaumeguy]

Hi there:

We are exploring the Additive Quantizer.

Looking at the documentation, it states:
At training time, the tables are trained sequentially by k-means at each step. The max_beam_size is also used for that".

However, in your eval script, the BS is not set prior to training.

Which one is correct? Did you find that it doesn't matter for training?

Lastly, is it possible to implement the parsing of the BS arg as part of the index factory (I see graphs that mention it but it seems that it's not implemented) e.g.

d = 1024
index_name = "PRQ128x3x8.BS8"
index = faiss.index_factory(d, index_name, faiss.METRIC_INNER_PRODUCT)

[junjieqi]

HI @guillaumeguy

There is a default max_beam_size=5 https://fburl.com/code/prz79r74.

you could set max_beam_size by following examples or demo here https://github.com/facebookresearch/faiss/blob/main/demos/demo_residual_quantizer.cpp#L81

rcq = faiss.index_factory(256, "RCQ3x8")
rcq.rq.max_beam_size = 3
rcq.train(item_emb)