Jeff Johnson
Jeff Johnson
What kind of GPU are you using? 40 GiB makes me think of A100, which really should require CUDA 11?
This is due to the geometric doubling behavior in faiss::gpu::DeviceVector's append, which happens when you call add on an index which already has data in it: https://github.com/facebookresearch/faiss/blob/main/faiss/gpu/utils/DeviceVector.cuh#L94 This is something...
This wouldn't be easy to implement, and I don't have the time to do it, but may be a long term feature to add.
> def radius_search(index, x, thresh, nprobe, max_k=3200, init_k=100, gpu=False): note, the maximum k-select value on the GPU is 2048 at present
Any GPU indices that share a single GpuResources object can only be used one at a time from any thread. A single GpuResources object can only be operated on sequentially,...
@cjnolet FYI with the Faiss/RAFT integration when the RAFT memory manager was being used, we were still reserving 1.5 GB of memory taken from the RAFT allocator which was given...
@cjnolet What is your thought on this change to the memory allocator? Currently in the repo, it was using (likely by accident) *both* the Faiss and RAFT memory allocator. The...
You seem to be passing a pytorch GPU tensor as input? You need to import faiss.contrib.torch_utils in order to allow this.
This should just be undoing https://github.com/facebookresearch/faiss/pull/2631 except instead of `_storage()` we use `untyped_storage()` https://pytorch.org/docs/stable/generated/torch.Tensor.untyped_storage.html#torch.Tensor.untyped_storage however, this means that this code needs to differ between pytorch < 2 and pytorch >...