mirror of
https://github.com/facebookresearch/faiss.git
synced 2025-06-03 21:54:02 +08:00
8dfd51d3a7
Summary: The pytorch interop code was in a test until now. However, it is better if people can rely on it to be updated when the API is updated. Therefore, we move it into contrib. Also added a README.md Reviewed By: wickedfoo Differential Revision: D23392962 fbshipit-source-id: 9b7c0e388a7ea3c0b73dc0018322138f49191673
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
import faiss
|
|
import torch
|
|
|
|
def swig_ptr_from_FloatTensor(x):
|
|
""" gets a Faiss SWIG pointer from a pytorch trensor (on CPU or GPU) """
|
|
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):
|
|
""" gets a Faiss SWIG pointer from a pytorch trensor (on CPU or GPU) """
|
|
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):
|
|
"""search xq in xb, without building an index"""
|
|
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)
|
|
|
|
args = faiss.GpuDistanceParams()
|
|
args.metric = metric
|
|
args.k = k
|
|
args.dims = d
|
|
args.vectors = xb_ptr
|
|
args.vectorsRowMajor = xb_row_major
|
|
args.numVectors = nb
|
|
args.queries = xq_ptr
|
|
args.queriesRowMajor = xq_row_major
|
|
args.numQueries = nq
|
|
args.outDistances = D_ptr
|
|
args.outIndices = I_ptr
|
|
faiss.bfKnn(res, args)
|
|
|
|
return D, I
|