Bug: Slow index.add about usearch

[vprelovac]

Thanks for looking into it.

1. 512-1024
2. l2sq
3. Python

This is the typical retrieval scenario in web search when LLMs are used.

I am passing exact=True for search as you see in my test code.

[vprelovac]

I can indeed confirm vastly better performance on my setup (by a factor 20). I can only say fantastic work! 🚀

Question: How does one get the distances?

[ashvardanian]

@vprelovac, oh, I see! So IndexFlatL2 is not actually an index. It just appends vectors and brute-forces on search. On tiny collections with 1000 entries, it makes sense. The kNN benchmarks are generally meaningful from 1M entries onwards. The correct comparison there would be against IndexHNSW from FAISS.

That being said, we can probably automatically switch to an exact search on tiny collections :) I will consider it in the next release.

[vprelovac]

the kNN benchmarks are generally meaningful from 1M entries onwards.

Except when they are not :) Our production use case is 500-50000 items, and the index has to be recreated every time. Every ms matters.

Is there a configuration where usearch can be faster than faiss on this use case?

[ashvardanian]

@vprelovac, probably. You can pass the additional exact=True for the' search' queries, which will switch to brute force.
As for construction, I've just added a task to the 1.0 TODO list.
Can you please provide more details about your setup?

1. What's the dimensionality of the vectors?
2. Which metrics do you use?
3. Which interface/platform do you prefer? Python, C++, WASM, ...

We may be able to adjust the implementation accordingly 🤗

[ashvardanian]

Hey, @vprelovac! I have implemented an exact search shortcut, which avoids index construction altogether. The interface is as simple as it gets:

from usearch.index import search, MetricKind

import numpy as np

# Set the seed for reproducibility
np.random.seed(42)

# Generate 500 random vectors of length 1024 with values between 0 and 1
vectors = np.random.rand(500, 1024).astype(np.float32)
vector = np.random.rand(1024).astype(np.float32)

search(vectors, vector, 50, MetricKind.L2sq, exact=True)

I have repeated your benchmark on my MacBook Pro with the M2 CPU, and the speed is higher than for FAISS, even without having a BLAS dependency and no SimSIMD optimizations for the L2 metric. This is going to be merged into 1.0. More optimizations coming soon.

[ashvardanian]

🎉 This issue has been resolved in version 0.23.0 🎉

The release is available on GitHub release

Your semantic-release bot 📦🚀

[ashvardanian]

Those should be in matches.distances. This method also supports batch search, so you can search for many entries at once. It also supports an additional threads argument, to specify the amount of CPU resources to allocate.

---

I am glad it worked so well, @vprelovac! We are also preparing major releases in UCall, UForm, and UStore, so stay tuned and don’t hesitate to share feedback. Myself and the team will be happy to find new ways we can improve our projects 🤗

[vprelovac]

Thanks this solves the use case for small size, real time, vector based search.

Given another use case of say 1-5M items, whre index can be built before search - how would you approach that?

[ashvardanian]

@vprelovac for that I would indeed use the Index class. I’d recommend experimenting with the dtype constructor argument. Try “i8”. If recall doesn’t drop, use that. Otherwise, use “f16”. It will be both space-efficient and fast. I am also experimenting with “i4”, which should work in case you use heavily quantized models. Will have more updates for this in the next couple of days. You can also control the balance between speed and accuracy using the connectivity constructor argument. Higher value, will probably lead to slower construction time, may result in higher accuracy, and will definitely use more memory.

[vprelovac]

@ashvardanian Sorry for reopening an old thread.

Finally had some more time to do testing with new version. Here is replication code.
Findings at the end.

!pip install faiss-cpu   git+https://github.com/vioshyvo/mrpt/  usearch


from usearch.index import search, MetricKind
import numpy as np
import mrpt
import faiss
import timeit
import matplotlib.pyplot as plt

def run_mrpt(vector, vectors, k=15):
  index = mrpt.MRPTIndex(vectors)
  res=index.exact_search(vector, k, return_distances=False)
  return res

def run_faiss(vector, vectors,k=15):
  index = faiss.IndexFlatL2(vectors.shape[-1])
  index.add(vectors)
  D, I = index.search(np.array([vector]), k)
  return I

def run_usearch(vector, vectors, k=15):
  matches= search (vectors, vector, k, MetricKind.L2sq, exact=True)
  return matches


sizes = [16, 24, 36, 54, 81, 122, 182, 273, 410, 615, 923, 1384, 2076]
#sizes = [3570, 4500, 5500, 6700, 7700, 8800, 9900, 11000, 15000,37481, 67466, 121440, 218591, 500000]

# Initialize lists to store the execution times
mrpt_times = []
faiss_times = []
usearch_times = []

number=20

# Benchmark the functions for each dataset size
for size in sizes:
    print(size)
    # Generate random dataset and query vector
    vectors = np.random.rand(size, 768).astype(np.float32)
    vector = np.random.rand(768).astype(np.float32)

    # Time the execution of each function
    mrpt_time = timeit.timeit(lambda: run_mrpt(vector, vectors, k=15), number=number)
    faiss_time = timeit.timeit(lambda: run_faiss(vector, vectors, k=15), number=number)
    usearch_time = timeit.timeit(lambda: run_usearch(vector, vectors, k=15), number=number)

    # Append the execution times to the corresponding lists
    mrpt_times.append(mrpt_time)
    faiss_times.append(faiss_time)
    usearch_times.append(usearch_time)

# Plot the results
plt.figure(figsize=(20, 12))
plt.plot(sizes, mrpt_times, label='MRPT', marker='o')
plt.plot(sizes, faiss_times, label='FAISS', marker='o')
plt.plot(sizes, usearch_times, label='usearch', marker='o')

plt.xlabel('Number of Vectors')
plt.ylabel('Execution Time (s)')
plt.title('Nearest Neighbor Search Performance')
plt.legend()
plt.grid()
plt.show()

What is interesting is that for small number of items (<2000), Faiss is still the king of the hill and both mrpt and usearch show great varience.

Going above that, Faiss starts to lag sinigifcantly, and MRPT consistently comes on top.

So perfect verctor search at the moment is a combination of Faiss for <2000 items and MRPT for more than that. Can you make one to rule them all? ;)

[ashvardanian]

For sure, @vprelovac! Thanks for sharing updates! Which hardware are you running on? Can you please check my SimSIMD library? I have upgraded it recently, but haven't merged into USearch yet.

[vprelovac]

This is just CPU on colab. Not sure what SimSIMD is, but if it affects the speed of usearch on CPU, it should be installed and used by default? Or an example using my code would be great.

[ashvardanian]

@vprelovac, I will double check in the next couple of days, in the meantime, here is what I mean by SimSIMD - it now has it's own light weight Python bindings. Here is a set of benchmarks for SimSIMD vs NumPy vs SciPy on a few different machines.

[ashvardanian]

Also, if you are running on recent hardware, you may prefer to use half-precision floating point numbers.

[ashvardanian]

@vprelovac, can you please check the newest release and please try half-precision as well 🤗

[vprelovac]

@ashvardanian Sure - Can you let me know if you tried it with the above code and do I need to make any changes to it?

[ashvardanian]

Here are the results I get running your script, @vprelovac:

If I understand correctly, MRPTIndex is just a wrapper around the C++ Eigen library. It's a pretty good library for numeric computations. It might be more accurate to compare it to the faiss.IndexFlatIP and the usearch.MetricKind.IP.

---

All three systems support batch queries. Both FAISS and USearch support half-precision. With half-precision, FAISS becomes much slower, but we retain good performance. Here are the charts I'm getting for batch size 10, enabling half-precision in USearch, leading to 2x lower memory consumption.

Here is the refreshed script:

!pip install faiss-cpu   git+https://github.com/vioshyvo/mrpt/  search

from usearch.index import search, MetricKind, ScalarKind
import numpy as np
import mrpt
import faiss
import timeit
import matplotlib.pyplot as plt

def run_mrpt(vector, vectors, k=15):
  index = mrpt.MRPTIndex(vectors)
  res=index.exact_search(vector, k)
  return res

def run_faiss(vector, vectors,k=15):
  index = faiss.IndexFlatIP(vectors.shape[-1])
  index.add(vectors)
  D, I = index.search(vector, k)
  return I

def run_usearch(vector, vectors, k=15):
  matches= search (vectors, vector, k, MetricKind.IP, exact=True)
  return matches


from usearch.compiled import hardware_acceleration

print("USearch hardware acceleration:", hardware_acceleration(dtype=ScalarKind.F16, ndim=768, metric_kind=MetricKind.IP), hardware_acceleration(dtype=ScalarKind.F32, ndim=768, metric_kind=MetricKind.IP))

count_dataset = [16, 24, 36, 54, 81, 122, 182, 273, 410, 615, 923, 1384, 2076]

# Initialize lists to store the execution times
mrpt_times = []
faiss_times = []
usearch_times = []

k = 10
count_repetitions = 100
count_queries = 10

# Benchmark the functions for each dataset size
for size in count_dataset:
    print(size)
    # Generate random dataset and query vector
    dataset = np.random.rand(size, 768).astype(np.float32)
    queries = np.random.rand(count_queries, 768).astype(np.float32)

    dataset_f16 = np.random.rand(size, 768).astype(np.float16)
    queries_f16 = np.random.rand(count_queries, 768).astype(np.float16)

    # Time the execution of each function
    mrpt_time = timeit.timeit(lambda: run_mrpt(queries, dataset, k=k), number=count_repetitions)
    faiss_time = timeit.timeit(lambda: run_faiss(queries, dataset, k=k), number=count_repetitions)
    usearch_time = timeit.timeit(lambda: run_usearch(queries_f16, dataset_f16, k=k), number=count_repetitions)

    # Append the execution times to the corresponding lists
    mrpt_times.append(mrpt_time)
    faiss_times.append(faiss_time)
    usearch_times.append(usearch_time)

# Plot the results
plt.figure(figsize=(20, 12))
plt.plot(count_dataset, mrpt_times, label='MRPT', marker='o')
plt.plot(count_dataset, faiss_times, label='FAISS', marker='o')
plt.plot(count_dataset, usearch_times, label='usearch', marker='o')

plt.xlabel('Number of Vectors')
plt.ylabel('Execution Time (s)')
plt.title('Nearest Neighbor Search Performance')
plt.legend()
plt.grid()
plt.show()