Initial commit

packages/leann-backend-hnsw/third_party/faiss/benchs/kmeans_mnist.py

@@ -0,0 +1,88 @@
+#! /usr/bin/env python2
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import print_function
+import numpy as np
+import time
+import faiss
+import sys
+
+
+# Get command-line arguments
+
+k = int(sys.argv[1])
+ngpu = int(sys.argv[2])
+
+# Load Leon's file format
+
+def load_mnist(fname):
+    print("load", fname)
+    f = open(fname)
+
+    header = np.fromfile(f, dtype='int8', count=4*4)
+    header = header.reshape(4, 4)[:, ::-1].copy().view('int32')
+    print(header)
+    nim, xd, yd = [int(x) for x in header[1:]]
+
+    data = np.fromfile(f, count=nim * xd * yd,
+                       dtype='uint8')
+
+    print(data.shape, nim, xd, yd)
+    data = data.reshape(nim, xd, yd)
+    return data
+
+basedir = "/path/to/mnist/data"
+
+x = load_mnist(basedir + 'mnist8m/mnist8m-patterns-idx3-ubyte')
+
+print("reshape")
+
+x = x.reshape(x.shape[0], -1).astype('float32')
+
+
+def train_kmeans(x, k, ngpu):
+    "Runs kmeans on one or several GPUs"
+    d = x.shape[1]
+    clus = faiss.Clustering(d, k)
+    clus.verbose = True
+    clus.niter = 20
+
+    # otherwise the kmeans implementation sub-samples the training set
+    clus.max_points_per_centroid = 10000000
+
+    res = [faiss.StandardGpuResources() for i in range(ngpu)]
+
+    flat_config = []
+    for i in range(ngpu):
+        cfg = faiss.GpuIndexFlatConfig()
+        cfg.useFloat16 = False
+        cfg.device = i
+        flat_config.append(cfg)
+
+    if ngpu == 1:
+        index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])
+    else:
+        indexes = [faiss.GpuIndexFlatL2(res[i], d, flat_config[i])
+                   for i in range(ngpu)]
+        index = faiss.IndexReplicas()
+        for sub_index in indexes:
+            index.addIndex(sub_index)
+
+    # perform the training
+    clus.train(x, index)
+    centroids = faiss.vector_float_to_array(clus.centroids)
+
+    obj = faiss.vector_float_to_array(clus.obj)
+    print("final objective: %.4g" % obj[-1])
+
+    return centroids.reshape(k, d)
+
+print("run")
+t0 = time.time()
+train_kmeans(x, k, ngpu)
+t1 = time.time()
+
+print("total runtime: %.3f s" % (t1 - t0))