Ondisk distributed index implementation

benchs/distributed_ondisk/README.md

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+# Distributed on-disk index for 1T-scale datasets 
+
+This is code corresponding to the description in [Indexing 1T vectors](https://github.com/facebookresearch/faiss/wiki/Indexing-1T-vectors). 
+All the code is in python 3 (and not compatible with Python 2). 
+The current code uses the Deep1B dataset for demonstration purposes, but can scale to 1000x larger.
+To run it, download the Deep1B dataset as explained [here](../#getting-deep1b), and edit paths to the dataset in the scripts. 
+
+## Distributed k-means
+
+To cluster 500M vectors to 10M centroids, it is useful to have a distriubuted k-means implementation. 
+The distribution simply consists in splitting the training vectors across machines (servers) and have them do the assignment. 
+The master/client then synthesizes the results and updates the centroids.
+
+The distributed k-means implementation here is based on 3 files:
+
+- [`rpc.py`](rpc.py) is a very simple remote procedure call implementation based on sockets and pickle. 
+It exposes the methods of an object on the server side so that they can be called from the client as if the object was local.
+
+- [`distributed_kmeans.py`](distributed_kmeans.py) contains the k-means implementation. 
+The main loop of k-means is re-implemented in python but follows closely the Faiss C++ implementation, and should not be significantly less efficient. 
+It relies on a `DatasetAssign` object that does the assignement to centrtoids, which is the bulk of the computation. 
+The object can be a Faiss CPU index, a GPU index or a set of remote GPU or CPU indexes.
+
+- [`run_on_cluster.bash`](run_on_cluster.bash) contains the shell code to run the distributed k-means on a cluster. 
+
+The distributed k-means works with a Python install that contains faiss and scipy (for sparse matrices).
+It clusters the training data of Deep1B, this can be changed easily to any file in fvecs, bvecs or npy format that contains the training set. 
+The training vectors may be too large to fit in RAM, but they are memory-mapped so that should not be a problem. 
+The file is also assumed to be accessible from all server machines with eg. a distributed file system.
+
+### Local tests 
+
+Edit `distibuted_kmeans.py` to point `testdata` to your local copy of the dataset. 
+
+Then, 4 levels of sanity check can be run: 
+```bash
+# reference Faiss C++ run
+python distributed_kmeans.py --test 0
+# using the Python implementation
+python distributed_kmeans.py --test 1
+# use the dispatch object (on local datasets)
+python distributed_kmeans.py --test 2
+# same, with GPUs
+python distributed_kmeans.py --test 3
+```
+The output should look like [This gist](https://gist.github.com/mdouze/ffa01fe666a9325761266fe55ead72ad).
+
+### Distributed sanity check
+
+To run the distributed k-means, `distibuted_kmeans.py` has to be run both on the servers (`--server` option) and client sides (`--client` option). 
+Edit the top of `run_on_cluster.bash` to set the path of the data to cluster. 
+
+Sanity checks can be run with 
+```bash 
+# non distributed baseline
+bash run_on_cluster.bash test_kmeans_0
+# using all the machine's GPUs
+bash run_on_cluster.bash test_kmeans_1
+# distrbuted run, with one local server per GPU
+bash run_on_cluster.bash test_kmeans_2
+```
+The test `test_kmeans_2` simulates a distributed run on a single machine by starting one server process per GPU and connecting to the servers via the rpc protocol. 
+The output should look like [this gist](https://gist.github.com/mdouze/5b2dc69b74579ecff04e1686a277d32e).
+
+
+
+### Distributed run
+
+The way the script can be distributed depends on the cluster's scheduling system. 
+Here we use Slurm, but it should be relatively easy to adapt to any scheduler that can allocate a set of matchines and start the same exectuable on all of them. 
+
+The command 
+```
+bash run_on_cluster.bash slurm_distributed_kmeans
+```
+asks SLURM for 5 machines with 4 GPUs each with the `srun` command. 
+All 5 machines run the script with the `slurm_within_kmeans_server` option. 
+They determine the number of servers and their own server id via the `SLURM_NPROCS` and `SLURM_PROCID` environment variables.
+
+All machines start `distributed_kmeans.py` in server mode for the slice of the dataset they are responsible for.
+
+In addition, the machine #0 also starts the client. 
+The client knows who are the other servers via the variable `SLURM_JOB_NODELIST`. 
+It connects to all clients and performs the clustering. 
+
+The output should look like [this gist](https://gist.github.com/mdouze/8d25e89fb4af5093057cae0f917da6cd).
+
+### Run used for deep1B
+
+For the real run, we run the clustering on 50M vectors to 1M centroids. 
+This is just a matter of using as many machines / GPUs as possible in setting the output centroids with the `--out filename` option.
+Then run
+```
+bash run_on_cluster.bash deep1b_clustering
+```
+
+The last lines of output read like: 
+```
+  Iteration 19 (898.92 s, search 875.71 s): objective=1.33601e+07 imbalance=1.303 nsplit=0
+ 0: writing centroids to /checkpoint/matthijs/ondisk_distributed/1M_centroids.npy
+```
+
+This means that the total training time was 899s, of which 876s were used for computation. 
+However, the computation includes the I/O overhead to the assignment servers. 
+In this implementation, the overhead of transmitting the data is non-negligible and so is the centroid computation stage. 
+This is due to the inefficient Python implementation and the RPC protocol that is not optimized for broadcast / gather (like MPI). 
+However, it is a simple implementation that should run on most clusters.
+
+## Making the trained index
+
+After the centroids are obtained, an empty trained index must be constructed. 
+This is done by: 
+
+- applying a pre-processing stage (a random rotation) to balance the dimensions of the vectors. This can be done after clustering, the clusters are just rotated as well.
+
+- wrapping the centroids into a HNSW index to speed up the CPU-based assignment of vectors
+
+- training the 6-bit scalar quantizer used to encode the vectors
+
+This is performed by the script [`make_trained_index.py`](make_trained_index.py). 
+
+## Building the index by slices
+
+We call the slices "vslisces" as they are vertical slices of the big matrix (see explanation in the wiki section [Split across datanbase partitions](https://github.com/facebookresearch/faiss/wiki/Indexing-1T-vectors#split-across-database-partitions)
+
+The script [make_index_vslice.py](make_index_vslice.py) makes an index for a subset of the vectors of the input data and stores it as an independent index. 
+There are 200 slices of 5M vectors each for Deep1B.
+It can be run in a brute-force parallel fashion, there is no constraint on ordering. 
+To run the script in parallel on a slurm cluster, use: 
+```
+bash run_on_cluster.bash make_index_vslices
+```
+For a real dataset, the data would be read from a DBMS. 
+In that case, reading the data and indexing it in parallel is worthwhile because reading is very slow.
+
+## Splitting accross inverted lists
+
+The 200 slices need to be merged together. 
+This is done with the script [merge_to_ondisk.py](merge_to_ondisk.py), that memory maps the 200 vertical slice indexes, extracts a subset of the inverted lists and writes them to a contiguous horizontal slice. 
+We slice the inverted lists into 50 horizontal slices. 
+This is run with 
+```
+bash run_on_cluster.bash make_index_hslices
+```
+
+## Querying the index
+
+At this point the index is ready. 
+The horizontal slices need to be loaded in the right order and combined into an index to be usable. 
+This is done in the [combined_index.py](combined_index.py) script. 
+It provides a `CombinedIndexDeep1B` object that contains an index object that can be searched. 
+To test, run: 
+```
+python combined_index.py
+```
+The output should look like: 
+```
+(faiss_1.5.2) matthijs@devfair0144:~/faiss_versions/faiss_1Tcode/faiss/benchs/distributed_ondisk$ python combined_index.py
+reading /checkpoint/matthijs/ondisk_distributed//hslices/slice49.faissindex
+loading empty index /checkpoint/matthijs/ondisk_distributed/trained.faissindex
+replace invlists
+loaded index of size  1000000000
+nprobe=1 1-recall@1=0.2904 t=12.35s
+nnprobe=10 1-recall@1=0.6499 t=17.67s
+nprobe=100 1-recall@1=0.8673 t=29.23s
+nprobe=1000 1-recall@1=0.9132 t=129.58s
+```
+ie. searching is a lot slower than from RAM. 
+
+## Distributed query
+
+To reduce the bandwidth required from the machine that does the queries, it is possible to split the search accross several search servers. 
+This way, only the effective results are returned to the main machine.
+
+The search client and server are implemented in [`search_server.py`](search_server.py). 
+It can be used as a script to start a search server for `CombinedIndexDeep1B` or as a module to load the clients.
+
+The search servers can be started with 
+```
+bash run_on_cluster.bash run_search_servers
+```
+(adjust to the number of servers that can be used). 
+
+Then an example of search client is [`distributed_query_demo.py`](distributed_query_demo.py). 
+It connects to the servers and assigns subsets of inverted lists to visit to each of them.
+
+A typical output is [this gist](https://gist.github.com/mdouze/1585b9854a9a2437d71f2b2c3c05c7c5). 
+The number in MiB indicates the amount of data that is read from disk to perform the search.
+In this case, the scale of the dataset is too small for the distributed search to have much impact, but on datasets > 10x larger, the difference becomes more significant.
+
+## Conclusion
+
+This code contains the core components to make an index that scales up to 1T vectors. 
+There are a few simplifications wrt. the index that was effectively used in [Indexing 1T vectors](https://github.com/facebookresearch/faiss/wiki/Indexing-1T-vectors).  

benchs/distributed_ondisk/combined_index.py

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+import os
+import faiss
+import numpy as np
+
+
+class CombinedIndex:
+    """
+    combines a set of inverted lists into a hstack
+    masks part of those lists
+    adds these inverted lists to an empty index that contains
+    the info on how to perform searches
+    """
+
+    def __init__(self, invlist_fnames, empty_index_fname,
+                 masked_index_fname=None):
+
+        self.indexes = indexes = []
+        ilv = faiss.InvertedListsPtrVector()
+
+        for fname in invlist_fnames:
+            if os.path.exists(fname):
+                print('reading', fname, end='\r', flush=True)
+                index = faiss.read_index(fname)
+                indexes.append(index)
+                il = faiss.extract_index_ivf(index).invlists
+            else:
+                assert False
+            ilv.push_back(il)
+        print()
+
+        self.big_il = faiss.VStackInvertedLists(ilv.size(), ilv.data())
+        if masked_index_fname:
+            self.big_il_base = self.big_il
+            print('loading', masked_index_fname)
+            self.masked_index = faiss.read_index(
+                masked_index_fname,
+                faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY)
+            self.big_il = faiss.MaskedInvertedLists(
+                faiss.extract_index_ivf(self.masked_index).invlists,
+                self.big_il_base)
+
+        print('loading empty index', empty_index_fname)
+        self.index = faiss.read_index(empty_index_fname)
+        ntotal = self.big_il.compute_ntotal()
+
+        print('replace invlists')
+        index_ivf = faiss.extract_index_ivf(self.index)
+        index_ivf.replace_invlists(self.big_il, False)
+        index_ivf.ntotal = self.index.ntotal = ntotal
+        index_ivf.parallel_mode = 1   # seems reasonable to do this all the time
+
+        quantizer = faiss.downcast_index(index_ivf.quantizer)
+        quantizer.hnsw.efSearch = 1024
+
+    ############################################################
+    # Expose fields and functions of the index as methods so that they
+    # can be called by RPC
+
+    def search(self, x, k):
+        return self.index.search(x, k)
+
+    def range_search(self, x, radius):
+        return self.index.range_search(x, radius)
+
+    def transform_and_assign(self, xq):
+        index = self.index
+
+        if isinstance(index, faiss.IndexPreTransform):
+            assert index.chain.size() == 1
+            vt = index.chain.at(0)
+            xq = vt.apply_py(xq)
+
+        # perform quantization
+        index_ivf = faiss.extract_index_ivf(index)
+        quantizer = index_ivf.quantizer
+        coarse_dis, list_nos = quantizer.search(xq, index_ivf.nprobe)
+        return xq, list_nos, coarse_dis
+
+
+    def ivf_search_preassigned(self, xq, list_nos, coarse_dis, k):
+        index_ivf = faiss.extract_index_ivf(self.index)
+        n, d = xq.shape
+        assert d == index_ivf.d
+        n2, d2 = list_nos.shape
+        assert list_nos.shape == coarse_dis.shape
+        assert n2 == n
+        assert d2 == index_ivf.nprobe
+        D = np.empty((n, k), dtype='float32')
+        I = np.empty((n, k), dtype='int64')
+        index_ivf.search_preassigned(
+            n, faiss.swig_ptr(xq), k,
+            faiss.swig_ptr(list_nos), faiss.swig_ptr(coarse_dis),
+            faiss.swig_ptr(D), faiss.swig_ptr(I), False)
+        return D, I
+
+
+    def ivf_range_search_preassigned(self, xq, list_nos, coarse_dis, radius):
+        index_ivf = faiss.extract_index_ivf(self.index)
+        n, d = xq.shape
+        assert d == index_ivf.d
+        n2, d2 = list_nos.shape
+        assert list_nos.shape == coarse_dis.shape
+        assert n2 == n
+        assert d2 == index_ivf.nprobe
+        res = faiss.RangeSearchResult(n)
+
+        index_ivf.range_search_preassigned(
+            n, faiss.swig_ptr(xq), radius,
+            faiss.swig_ptr(list_nos), faiss.swig_ptr(coarse_dis),
+            res)
+
+        lims = faiss.rev_swig_ptr(res.lims, n + 1).copy()
+        nd = int(lims[-1])
+        D = faiss.rev_swig_ptr(res.distances, nd).copy()
+        I = faiss.rev_swig_ptr(res.labels, nd).copy()
+        return lims, D, I
+
+    def set_nprobe(self, nprobe):
+        index_ivf = faiss.extract_index_ivf(self.index)
+        index_ivf.nprobe = nprobe
+
+    def set_parallel_mode(self, pm):
+        index_ivf = faiss.extract_index_ivf(self.index)
+        index_ivf.parallel_mode = pm
+
+    def get_ntotal(self):
+        return self.index.ntotal
+
+    def set_prefetch_nthread(self, nt):
+        for idx in self.indexes:
+            il = faiss.downcast_InvertedLists(
+                faiss.extract_index_ivf(idx).invlists)
+            il.prefetch_nthread
+            il.prefetch_nthread = nt
+
+    def set_omp_num_threads(self, nt):
+        faiss.omp_set_num_threads(nt)
+
+class CombinedIndexDeep1B(CombinedIndex):
+    """ loads a CombinedIndex with the data from the big photodna index """
+
+    def __init__(self):
+        # set some paths
+        workdir = "/checkpoint/matthijs/ondisk_distributed/"
+
+        # empty index with the proper quantizer
+        indexfname = workdir + 'trained.faissindex'
+
+        # index that has some invlists that override the big one
+        masked_index_fname = None
+        invlist_fnames = [
+            '%s/hslices/slice%d.faissindex' % (workdir, i)
+            for i in range(50)
+        ]
+        CombinedIndex.__init__(self, invlist_fnames, indexfname, masked_index_fname)
+
+
+def ivecs_read(fname):
+    a = np.fromfile(fname, dtype='int32')
+    d = a[0]
+    return a.reshape(-1, d + 1)[:, 1:].copy()
+
+
+def fvecs_read(fname):
+    return ivecs_read(fname).view('float32')
+
+
+if __name__ == '__main__':
+    import time
+    ci = CombinedIndexDeep1B()
+    print('loaded index of size ', ci.index.ntotal)
+
+    deep1bdir = "/datasets01_101/simsearch/041218/deep1b/"
+
+    xq = fvecs_read(deep1bdir + "deep1B_queries.fvecs")
+    gt_fname = deep1bdir + "deep1B_groundtruth.ivecs"
+    gt = ivecs_read(gt_fname)
+
+    for nprobe in 1, 10, 100, 1000:
+        ci.set_nprobe(nprobe)
+        t0 = time.time()
+        D, I = ci.search(xq, 100)
+        t1 = time.time()
+        print('nprobe=%d 1-recall@1=%.4f t=%.2fs' % (
+            nprobe, (I[:, 0] == gt[:, 0]).sum() / len(xq),
+            t1 - t0
+        ))