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Update TileDB benchmarks #2

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merged 3 commits into from
May 13, 2024
Merged

Update TileDB benchmarks #2

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d4069c2
fix benchmarks
jparismorgan Apr 5, 2024
2065290
cleanup code
jparismorgan Apr 5, 2024
0502021
add todos
jparismorgan May 13, 2024
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10 changes: 3 additions & 7 deletions ann_benchmarks/algorithms/tiledb/Dockerfile
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Original file line number Diff line number Diff line change
@@ -1,8 +1,4 @@
FROM tiledb_vs
FROM ann-benchmarks

RUN mamba install -y ansicolors docker-py h5py matplotlib numpy pyyaml psutil scipy scikit-learn jinja2 pandas

WORKDIR /home/app
COPY requirements.txt run_algorithm.py ./
RUN pip3 install -r requirements.txt
ENTRYPOINT ["python", "-u", "run_algorithm.py"]
RUN pip install tiledb tiledb-vector-search
RUN python3 -c 'import tiledb.vector_search'
48 changes: 35 additions & 13 deletions ann_benchmarks/algorithms/tiledb/module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,8 @@
import tiledb

from tiledb.vector_search.ingestion import ingest
from tiledb.vector_search.index import IVFFlatIndex
from tiledb.vector_search import IVFFlatIndex
from tiledb.cloud.dag import Mode
import numpy as np
import multiprocessing

Expand All @@ -15,17 +16,35 @@ class TileDBIVFFlat(BaseANN):
def __init__(self, metric, n_list):
self._n_list = n_list
self._metric = metric
self.MAX_UINT64 = np.iinfo(np.dtype("uint64")).max

def query(self, v, n):
if self._metric == 'angular':
raise NotImplementedError()
self.res =self.index.query(np.array([v]).astype(numpy.float32), k=n, nthreads=multiprocessing.cpu_count(), nprobe=min(self._n_probe,self._n_list), use_nuv_implementation=True)
return self.res[0]

# query() returns a tuple of (distances, ids).
ids = self.index.query(
np.array([v]).astype(numpy.float32),
k=n,
nthreads=multiprocessing.cpu_count(),
nprobe=min(self._n_probe,self._n_list),
)[1][0]
# Fix for 'OverflowError: Python int too large to convert to C long'.
ids[ids == self.MAX_UINT64] = 0
return ids

def batch_query(self, X, n):
if self._metric == 'angular':
raise NotImplementedError()
self.res =self.index.query(X.astype(numpy.float32), k=n, nthreads=multiprocessing.cpu_count(), nprobe=min(self._n_probe, self._n_list), use_nuv_implementation=True)
# query() returns a tuple of (distances, ids).
self.res = self.index.query(
X.astype(numpy.float32),
k=n,
nthreads=multiprocessing.cpu_count(),
nprobe=min(self._n_probe, self._n_list),
)[1]
# Fix for 'OverflowError: Python int too large to convert to C long'.
self.res[self.res == self.MAX_UINT64] = 0

def get_batch_results(self):
return self.res
Expand All @@ -47,15 +66,18 @@ def fit(self, X):
elif X.dtype == "float32":
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We can update this to use the numpy arrays directly for ingestion

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Thanks, added this as a TODO and will fix this the next time I set up an EC2 instance and run this. Doing this b/c it seems nice to get this PR merged versus leaving in flux until then.

source_type = "F32BIN"
maxtrain = min(50 * self._n_list, X.shape[0])
self.index = ingest(index_type="IVF_FLAT",
array_uri=array_uri,
source_uri=source_uri,
source_type=source_type,
size=X.shape[0],
training_sample_size=maxtrain,
partitions=self._n_list,
input_vectors_per_work_item=100000000,
)
self.index = ingest(
index_type="IVF_FLAT",
index_uri=array_uri,
source_uri=source_uri,
source_type=source_type,
size=X.shape[0],
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I think we can just use the defaults for most params in ingestion

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Thanks, added this as a TODO and will fix this the next time I set up an EC2 instance and run this. Doing this b/c it seems nice to get this PR merged versus leaving in flux until then.

training_sample_size=maxtrain,
partitions=self._n_list,
input_vectors_per_work_item=100000000,
mode=Mode.LOCAL
)
# memory_budget=-1 will load the data into main memory.
self.index = IVFFlatIndex(uri=array_uri, dtype=X.dtype, memory_budget=-1)
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dtype, memory_budget args could be removed as this is the default behavior.

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Thanks, added this as a TODO and will fix this the next time I set up an EC2 instance and run this. Doing this b/c it seems nice to get this PR merged versus leaving in flux until then.


def set_query_arguments(self, n_probe):
Expand Down