TorchPQ
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.
Install
First install a version of CuPy library that matches your CUDA version
pip install cupy-cuda90
pip install cupy-cuda100
pip install cupy-cuda101
...
Then install TorchPQ
pip install torchpq
for a full list of cupy-cuda versions, please go to Installation Guide
Quick Start
IVFPQ
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.
Training
from torchpq import IVFPQ
n_data = 1000000 # number of data points
d_vector = 128 # dimentionality / number of features
index = IVFPQ(
d_vector=d_vector,
n_subvectors=64,
n_cq_clusters=1024,
n_pq_clusters=256,
blocksize=128,
distance="euclidean",
)
x = torch.randn(d_vector, n_data, device="cuda:0")
index.train(x)
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_cq_clusters: number of coarse quantizer clusters
- n_pq_clusters: number of product quantizer clusters, this is assumed to be 256 throughout the entire project, and should NOT be changed.
- blocksize: initial capacity assigned to each voronoi cell of coarse quantizer.
n_cq_clusters * blocksizeis 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 forblocksizeis 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
Adding new vectors
ids = torch.arange(n_data, device="cuda")
index.add(x, input_ids=ids)
Each ID in ids needs to be a unique int64 (torch.long) value that identifies a vector in x. if input_ids is not provided, it will be set to torch.arange(n_data, device="cuda") + previous_max_id
Removing vectors
index.remove(ids)
index.remove(ids) will virtually remove vectors with specified ids from storage. It ignores ids that doesn't exist.
Topk search
index.n_probe = 32
n_query = 10000
query = torch.randn(d_vector, n_query, device="cuda:0")
topk_values, topk_ids = index.topk(query, 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.
Encode and Decode
you can use IVFPQ as a vector codec for lossy compression of vectors
code = index.encode(query) # compression
reconstruction = index.decode(code) # reconstruction
Save and Load
Most 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))
Clustering
K-means
from torchpq.kmeans 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.
Multiple concurrent K-means
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.kmeans 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)
Prediction with K-means
labels = kmeans.predict(x)
Benchmark
All experiments were performed with a Tesla T4 GPU.
SIFT1M
IVFPQ
Faiss is one of the most well known ANN search libraries, and it also has a GPU implementation of IVFPQ, so we did some comparison experiments with faiss.
How to read the plot:
- the plot format follows the style of ann-benchmarks
- X axis is recall@1, Y axis is queries/second
- the closer to the top right corner the better
- indexes with same parameters from different libraries have similar colors.
- different libraries have different line styles (TorchPQ is solid line with circle marker, faiss is dashed line with triangle marker)
- each node on the line represents a different n_probe, starting from 1 at the left most node, and multiplied by 2 at the next node. (n_probe = 1,2,4,8,16,...)
Summary:
- for all the IVF16384 variants, torchpq outperforms faiss when n_probe > 16.
- for IVF4096, torchpq has lower recall@1 compared to faiss, this could be caused by not encoding residuals. An option to encode residuals will be added soon.
IVFPQ+R
GIST1M
coming soon...
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