[Feature]: Support Double, Float16 and BF16 vectors

[xiaofan-luan]

Is there an existing issue for this?

• I have searched the existing issues

Is your feature request related to a problem? Please describe.

There are many different vector types based on models.
So far what we received most is double, float16, BF16, double and BF16 is on top priority.
Anyone interested on it please help

Describe the solution you'd like.

No response

Describe an alternate solution.

No response

Anything else? (Additional Context)

No response

[jon-chuang]

/assign

[jon-chuang]

@xiaofan-luan could I ask if it is wrong to convert the embedding to a float32, which I think has better numerical performance on most CPU unless hardware support exists?

Or, is the purpose of this issue to support storage of such formats assuming that the compute nodes have the correct compute (e.g. GPU or the right Xeon chipset) to handle operations in those datatypes?

If so, do we need to implement fallback by e.g. emulation or casting when the appropriate compute support is missing? Pytorch handles by autocasting.

[jon-chuang]

Duplicate: #22132

[jon-chuang]

btw, bfloat16 does not exist on faiss: https://github.com/facebookresearch/faiss/wiki/How-to-make-Faiss-run-faster, and I believe not in Annoy or HNSWLib either

But it supports float16 and we can compile it back in: #2828

[jiaoew1991]

Welcome @jon-chuang , You can implement float16 first, we can discuss about bf16 later. 😄

[jiaoew1991]

/unassign @jon-chuang

[jiaoew1991]

We can break down the steps into the following:

• segcore supports brute-force search for float16.
• Go distributed layer supports float16.
• Knowhere supports float16 type indexing.
• SDKs support float16.