faiss_utils.py

import os
import numpy as np
import faiss
import torch

def swig_ptr_from_FloatTensor(x):
    assert x.is_contiguous()
    assert x.dtype == torch.float32
    return faiss.cast_integer_to_float_ptr(
        x.storage().data_ptr() + x.storage_offset() * 4)

def swig_ptr_from_LongTensor(x):
    assert x.is_contiguous()
    assert x.dtype == torch.int64, 'dtype=%s' % x.dtype
    return faiss.cast_integer_to_long_ptr(
        x.storage().data_ptr() + x.storage_offset() * 8)

def search_index_pytorch(index, x, k, D=None, I=None):
    """call the search function of an index with pytorch tensor I/O (CPU
    and GPU supported)"""
    assert x.is_contiguous()
    n, d = x.size()
    assert d == index.d

    if D is None:
        D = torch.empty((n, k), dtype=torch.float32, device=x.device)
    else:
        assert D.size() == (n, k)

    if I is None:
        I = torch.empty((n, k), dtype=torch.int64, device=x.device)
    else:
        assert I.size() == (n, k)
    torch.cuda.synchronize()
    xptr = swig_ptr_from_FloatTensor(x)
    Iptr = swig_ptr_from_LongTensor(I)
    Dptr = swig_ptr_from_FloatTensor(D)
    index.search_c(n, xptr,
                   k, Dptr, Iptr)
    torch.cuda.synchronize()
    return D, I

def search_raw_array_pytorch(res, xb, xq, k, D=None, I=None,
                             metric=faiss.METRIC_L2):
    assert xb.device == xq.device

    nq, d = xq.size()
    if xq.is_contiguous():
        xq_row_major = True
    elif xq.t().is_contiguous():
        xq = xq.t()    # I initially wrote xq:t(), Lua is still haunting me :-)
        xq_row_major = False
    else:
        raise TypeError('matrix should be row or column-major')

    xq_ptr = swig_ptr_from_FloatTensor(xq)

    nb, d2 = xb.size()
    assert d2 == d
    if xb.is_contiguous():
        xb_row_major = True
    elif xb.t().is_contiguous():
        xb = xb.t()
        xb_row_major = False
    else:
        raise TypeError('matrix should be row or column-major')
    xb_ptr = swig_ptr_from_FloatTensor(xb)

    if D is None:
        D = torch.empty(nq, k, device=xb.device, dtype=torch.float32)
    else:
        assert D.shape == (nq, k)
        assert D.device == xb.device

    if I is None:
        I = torch.empty(nq, k, device=xb.device, dtype=torch.int64)
    else:
        assert I.shape == (nq, k)
        assert I.device == xb.device

    D_ptr = swig_ptr_from_FloatTensor(D)
    I_ptr = swig_ptr_from_LongTensor(I)

    faiss.bruteForceKnn(res, metric,
                xb_ptr, xb_row_major, nb,
                xq_ptr, xq_row_major, nq,
                d, k, D_ptr, I_ptr)

    return D, I

def index_init_gpu(ngpus, feat_dim):
    flat_config = []
    for i in range(ngpus):
        cfg = faiss.GpuIndexFlatConfig()
        cfg.useFloat16 = False
        cfg.device = i
        flat_config.append(cfg)

    res = [faiss.StandardGpuResources() for i in range(ngpus)]
    indexes = [faiss.GpuIndexFlatL2(res[i], feat_dim, flat_config[i]) for i in range(ngpus)]
    index = faiss.IndexShards(feat_dim)
    for sub_index in indexes:
        index.add_shard(sub_index)
    index.reset()
    return index

def index_init_cpu(feat_dim):
    return faiss.IndexFlatL2(feat_dim)