Spaces:
Sleeping
Sleeping
File size: 8,205 Bytes
a006afd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
import multiprocessing
from concurrent.futures import Future, ThreadPoolExecutor, wait
import random
import threading
from typing import Any, Dict, List, Optional, Set, Tuple, cast
import numpy as np
from chromadb.api import API
import chromadb.test.property.invariants as invariants
from chromadb.test.property.strategies import RecordSet
from chromadb.test.property.strategies import test_hnsw_config
from chromadb.types import Metadata
def generate_data_shape() -> Tuple[int, int]:
N = random.randint(10, 10000)
D = random.randint(10, 256)
return (N, D)
def generate_record_set(N: int, D: int) -> RecordSet:
ids = [str(i) for i in range(N)]
metadatas: List[Dict[str, int]] = [{f"{i}": i} for i in range(N)]
documents = [f"doc {i}" for i in range(N)]
embeddings = np.random.rand(N, D).tolist()
# Create a normalized record set to compare against
normalized_record_set: RecordSet = {
"ids": ids,
"embeddings": embeddings,
"metadatas": metadatas, # type: ignore
"documents": documents,
}
return normalized_record_set
# Hypothesis is bad at generating large datasets so we manually generate data in
# this test to test multithreaded add with larger datasets
def _test_multithreaded_add(api: API, N: int, D: int, num_workers: int) -> None:
records_set = generate_record_set(N, D)
ids = records_set["ids"]
embeddings = records_set["embeddings"]
metadatas = records_set["metadatas"]
documents = records_set["documents"]
print(f"Adding {N} records with {D} dimensions on {num_workers} workers")
# TODO: batch_size and sync_threshold should be configurable
api.reset()
coll = api.create_collection(name="test", metadata=test_hnsw_config)
with ThreadPoolExecutor(max_workers=num_workers) as executor:
futures: List[Future[Any]] = []
total_sent = -1
while total_sent < len(ids):
# Randomly grab up to 10% of the dataset and send it to the executor
batch_size = random.randint(1, N // 10)
to_send = min(batch_size, len(ids) - total_sent)
start = total_sent + 1
end = total_sent + to_send + 1
if embeddings is not None and len(embeddings[start:end]) == 0:
break
future = executor.submit(
coll.add,
ids=ids[start:end],
embeddings=embeddings[start:end] if embeddings is not None else None,
metadatas=metadatas[start:end] if metadatas is not None else None, # type: ignore
documents=documents[start:end] if documents is not None else None,
)
futures.append(future)
total_sent += to_send
wait(futures)
for future in futures:
exception = future.exception()
if exception is not None:
raise exception
# Check that invariants hold
invariants.count(coll, records_set)
invariants.ids_match(coll, records_set)
invariants.metadatas_match(coll, records_set)
invariants.no_duplicates(coll)
# Check that the ANN accuracy is good
# On a random subset of the dataset
query_indices = random.sample([i for i in range(N)], 10)
n_results = 5
invariants.ann_accuracy(
coll,
records_set,
n_results=n_results,
query_indices=query_indices,
)
def _test_interleaved_add_query(api: API, N: int, D: int, num_workers: int) -> None:
"""Test that will use multiple threads to interleave operations on the db and verify they work correctly"""
api.reset()
coll = api.create_collection(name="test", metadata=test_hnsw_config)
records_set = generate_record_set(N, D)
ids = cast(List[str], records_set["ids"])
embeddings = cast(List[float], records_set["embeddings"])
metadatas = cast(List[Metadata], records_set["metadatas"])
documents = records_set["documents"]
added_ids: Set[str] = set()
lock = threading.Lock()
print(f"Adding {N} records with {D} dimensions on {num_workers} workers")
def perform_operation(
operation: int, ids_to_modify: Optional[List[str]] = None
) -> None:
"""Perform a random operation on the collection"""
if operation == 0:
assert ids_to_modify is not None
indices_to_modify = [ids.index(id) for id in ids_to_modify]
# Add a subset of the dataset
if len(indices_to_modify) == 0:
return
coll.add(
ids=ids_to_modify,
embeddings=[embeddings[i] for i in indices_to_modify]
if embeddings is not None
else None,
metadatas=[metadatas[i] for i in indices_to_modify]
if metadatas is not None
else None,
documents=[documents[i] for i in indices_to_modify]
if documents is not None
else None,
)
with lock:
added_ids.update(ids_to_modify)
elif operation == 1:
currently_added_ids = []
n_results = 5
with lock:
currently_added_ids = list(added_ids.copy())
currently_added_indices = [ids.index(id) for id in currently_added_ids]
if (
len(currently_added_ids) == 0
or len(currently_added_indices) < n_results
):
return
# Query the collection, we can't test the results because we want to interleave
# queries and adds. We cannot do so without a lock and serializing the operations
# which would defeat the purpose of this test. Instead we interleave queries and
# adds and check the invariants at the end
query_indices = random.sample(
currently_added_indices,
min(10, len(currently_added_indices)),
)
query_vectors = [embeddings[i] for i in query_indices]
# Query the collections
coll.query(
query_vectors,
n_results=n_results,
)
with ThreadPoolExecutor(max_workers=num_workers) as executor:
futures: List[Future[Any]] = []
total_sent = -1
while total_sent < len(ids) - 1:
operation = random.randint(0, 2)
if operation == 0:
# Randomly grab up to 10% of the dataset and send it to the executor
batch_size = random.randint(1, N // 10)
to_send = min(batch_size, len(ids) - total_sent)
start = total_sent + 1
end = total_sent + to_send + 1
future = executor.submit(perform_operation, operation, ids[start:end])
futures.append(future)
total_sent += to_send
elif operation == 1:
future = executor.submit(
perform_operation,
operation,
)
futures.append(future)
wait(futures)
for future in futures:
exception = future.exception()
if exception is not None:
raise exception
# Check that invariants hold
invariants.count(coll, records_set)
invariants.ids_match(coll, records_set)
invariants.metadatas_match(coll, records_set)
invariants.no_duplicates(coll)
# Check that the ANN accuracy is good
# On a random subset of the dataset
query_indices = random.sample([i for i in range(N)], 10)
n_results = 5
invariants.ann_accuracy(
coll,
records_set,
n_results=n_results,
query_indices=query_indices,
)
def test_multithreaded_add(api: API) -> None:
for i in range(3):
num_workers = random.randint(2, multiprocessing.cpu_count() * 2)
N, D = generate_data_shape()
_test_multithreaded_add(api, N, D, num_workers)
def test_interleaved_add_query(api: API) -> None:
for i in range(3):
num_workers = random.randint(2, multiprocessing.cpu_count() * 2)
N, D = generate_data_shape()
_test_interleaved_add_query(api, N, D, num_workers)
|