TorchPQ is a python library for Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) on GPU using Product Quantization (PQ) algorithm. TorchPQ is implemented mainly with PyTorch, with some extra CUDA kernels to accelerate clustering, indexing and searching.
- make sure you have the latest version of PyTorch installed: https://pytorch.org/get-started/locally/
- install or upgrade to CUDA toolkit 11.0 or greater
- install a version of CuPy library that matches your CUDA toolkit version
pip install cupy-cuda110
pip install cupy-cuda111
pip install cupy-cuda112
...
for a full list of cupy-cuda versions, please go to Installation Guide
- install TorchPQ
pip install torchpq
InVerted File Product Quantization (IVFPQ) is a type of ANN search algorithm that is designed to do fast and efficient vector search in million, or even billion scale vector sets. check the original paper for more details.
from torchpq.index import IVFPQIndex
import torch
n_data = 1000000 # number of data points
d_vector = 128 # dimentionality / number of features
index = IVFPQIndex(
d_vector=d_vector,
n_subvectors=64,
n_cells=1024,
initial_size=2048,
distance="euclidean",
)
trainset = torch.randn(d_vector, n_data, device="cuda:0")
index.train(trainset)There are some important parameters that need to be explained:
- d_vector: dimentionality of input vectors. there are 2 constraints on
d_vector: (1) it needs to be divisible byn_subvectors; (2) it needs to be a multiple of 4.* - n_subvectors: number of subquantizers, essentially this is the byte size of each quantized vector, 64 byte per vector in the above example.**
- n_cells: number of coarse quantizer clusters
- initial_size: initial capacity assigned to each voronoi cell of coarse quantizer.
n_cells * initial_sizeis the number of vectors that can be stored initially. if any cell has reached its capacity, that cell will be automatically expanded. If you need to add vectors frequently, a larger value forinitial_sizeis recommended.
Remember that the shape of any tensor that contains data points has to be [d_vector, n_data].
* the second constraint could be removed in the future
** actual byte size would be (n_subvectors+9) bytes, 8 bytes for ID and 1 byte for is_empty
baseset = torch.randn(d_vector, n_data, device="cuda:0")
ids = torch.arange(n_data, device="cuda")
index.add(baseset, ids=ids)Each ID in ids needs to be a unique int64 (torch.long) value that identifies a vector in x.
if ids is not provided, it will be set to torch.arange(n_data, device="cuda") + previous_max_id
index.remove(ids=ids)index.remove(ids=ids) will virtually remove vectors with specified ids from storage.
It ignores ids that doesn't exist.
index.n_probe = 32
n_query = 10000
queryset = torch.randn(d_vector, n_query, device="cuda:0")
topk_values, topk_ids = index.search(queryset, k=100)- when
distance="inner",topk_valuesare inner product of queries and topk closest data points. - when
distance="euclidean",topk_valuesare negative squared L2 distance between queries and topk closest data points. - when
distance="manhattan",topk_valuesare negative L1 distance between queries and topk closest data points. - when
distance="cosine",topk_valuesare cosine similarity between queries and topk closest data points.
you can use IVFPQ as a vector codec for lossy compression of vectors
code = index.encode(queryset) # compression
reconstruction = index.decode(code) # reconstructionMost of the TorchPQ modules are inherited from torch.nn.Module, this means you can save and load them just like a regular pytorch model.
# Save to PATH
torch.save(index.state_dict(), PATH)
# Load from PATH
index.load_state_dict(torch.load(PATH))from torchpq.clustering import KMeans
import torch
n_data = 1000000 # number of data points
d_vector = 128 # dimentionality / number of features
x = torch.randn(d_vector, n_data, device="cuda")
kmeans = KMeans(n_clusters=4096, distance="euclidean")
labels = kmeans.fit(x)Notice that the shape of the tensor that contains data points has to be [d_vector, n_data], this is consistant in TorchPQ.
Sometimes, we have multiple independent datasets that need to be clustered, instead of running multiple KMeans sequentianlly, we can perform multiple kmeans concurrently with MultiKMeans
from torchpq.clustering import MultiKMeans
import torch
n_data = 1000000
n_kmeans = 16
d_vector = 64
x = torch.randn(n_kmeans, d_vector, n_data, device="cuda")
kmeans = MultiKMeans(n_clusters=256, distance="euclidean")
labels = kmeans.fit(x)labels = kmeans.predict(x)
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