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ebe82db4fe
/kind improvement <!-- This is an auto-generated comment: release notes by coderabbit.ai --> - Core invariant: test infrastructure treats insertion granularity as orthogonal to data semantics—bulk generation gen_row_data_by_schema(nb=2000, start=0, random_pk=False) yields the same sequential PKs and vector payloads as prior multi-batch inserts, so tests relying on collection lifecycle, flush, index build, load and search behave identically. - What changed / simplified: added a full HNSW_PQ parameterized test suite (tests/python_client/testcases/indexes/idx_hnsw_pq.py and test_hnsw_pq.py) and simplified HNSW_SQ test insertion by replacing looped per-batch generation+insert with a single bulk gen_row_data_by_schema(...) + insert. The per-batch PK sequencing and repeated vector generation were redundant for correctness and were removed to reduce complexity. - Why this does NOT cause data loss or behavior regression: the post-insert code paths remain unchanged—tests still call client.flush(), create_index(...), util.wait_for_index_ready(), collection.load(), and perform searches that assert describe_index and search outputs. Because start=0 and random_pk=False reproduce identical sequential PKs (0..1999) and the same vectors, index creation and search validation operate on identical data and index parameters, preserving previous assertions and outcomes. - New capability: comprehensive HNSW_PQ coverage (build params: M, efConstruction, m, nbits, refine, refine_type; search params: ef, refine_k) across vector types (FLOAT_VECTOR, FLOAT16_VECTOR, BFLOAT16_VECTOR, INT8_VECTOR) and metrics (L2, IP, COSINE), implemented as data-driven tests to validate success and failure/error messages for boundary, type-mismatch and inter-parameter constraints. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: zilliz <jiaming.li@zilliz.com>
258 lines
11 KiB
Python
258 lines
11 KiB
Python
import logging
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from utils.util_pymilvus import *
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from common.common_type import CaseLabel, CheckTasks
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from common import common_type as ct
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from common import common_func as cf
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from base.client_v2_base import TestMilvusClientV2Base
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import pytest
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from idx_hnsw_pq import HNSW_PQ
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index_type = "HNSW_PQ"
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success = "success"
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pk_field_name = 'id'
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vector_field_name = 'vector'
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dim = ct.default_dim
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default_nb = 2000
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default_build_params = {"M": 16, "efConstruction": 200, "m": 64, "nbits": 8}
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default_search_params = {"ef": 64, "refine_k": 1}
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class TestHnswPQBuildParams(TestMilvusClientV2Base):
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("params", HNSW_PQ.build_params)
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def test_hnsw_pq_build_params(self, params):
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"""
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Test the build params of HNSW_PQ index
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
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self.create_collection(client, collection_name, schema=schema)
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all_rows = cf.gen_row_data_by_schema(
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nb=default_nb,
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schema=schema,
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start=0,
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random_pk=False
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)
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self.insert(client, collection_name, all_rows)
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self.flush(client, collection_name)
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# create index
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build_params = params.get("params", None)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name=vector_field_name,
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metric_type=cf.get_default_metric_for_vector_type(vector_type=DataType.FLOAT_VECTOR),
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index_type=index_type,
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params=build_params)
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# build index
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if params.get("expected", None) != success:
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self.create_index(client, collection_name, index_params,
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check_task=CheckTasks.err_res,
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check_items=params.get("expected"))
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else:
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self.create_index(client, collection_name, index_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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# load collection
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self.load_collection(client, collection_name)
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# search
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nq = 2
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search(client, collection_name, search_vectors,
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search_params=default_search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": nq,
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"limit": ct.default_limit,
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"pk_name": pk_field_name})
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# verify the index params are persisted
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idx_info = client.describe_index(collection_name, vector_field_name)
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if build_params is not None:
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for key, value in build_params.items():
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if value is not None:
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assert key in idx_info.keys()
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assert str(value) in idx_info.values()
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("vector_data_type", ct.all_vector_types)
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def test_hnsw_pq_on_all_vector_types(self, vector_data_type):
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"""
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Test HNSW_PQ index on all the vector types and metrics
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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if vector_data_type == DataType.SPARSE_FLOAT_VECTOR:
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schema.add_field(vector_field_name, datatype=vector_data_type)
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else:
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schema.add_field(vector_field_name, datatype=vector_data_type, dim=dim)
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self.create_collection(client, collection_name, schema=schema)
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all_rows = cf.gen_row_data_by_schema(
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nb=default_nb,
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schema=schema,
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start=0,
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random_pk=False
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)
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self.insert(client, collection_name, all_rows)
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self.flush(client, collection_name)
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# create index
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index_params = self.prepare_index_params(client)[0]
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metric_type = cf.get_default_metric_for_vector_type(vector_data_type)
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index_params.add_index(field_name=vector_field_name,
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metric_type=metric_type,
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index_type=index_type,
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params=default_build_params)
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if vector_data_type not in HNSW_PQ.supported_vector_types:
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self.create_index(client, collection_name, index_params,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999,
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"err_msg": f"can't build with this index HNSW_PQ: invalid parameter"})
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else:
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self.create_index(client, collection_name, index_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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# load collection
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self.load_collection(client, collection_name)
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# search
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nq = 2
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=vector_data_type)
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self.search(client, collection_name, search_vectors,
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search_params=default_search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": nq,
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"limit": ct.default_limit,
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"pk_name": pk_field_name})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("metric", HNSW_PQ.supported_metrics)
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def test_hnsw_pq_on_all_metrics(self, metric):
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"""
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Test the search params of HNSW_PQ index
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
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self.create_collection(client, collection_name, schema=schema)
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all_rows = cf.gen_row_data_by_schema(
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nb=default_nb,
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schema=schema,
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start=0,
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random_pk=False
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)
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self.insert(client, collection_name, all_rows)
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self.flush(client, collection_name)
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# create index
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name=vector_field_name,
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metric_type=metric,
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index_type=index_type,
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params=default_build_params)
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self.create_index(client, collection_name, index_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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# load collection
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self.load_collection(client, collection_name)
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# search
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nq = 2
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search(client, collection_name, search_vectors,
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search_params=default_search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": nq,
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"limit": ct.default_limit,
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"pk_name": pk_field_name})
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@pytest.mark.xdist_group("TestHnswPQSearchParams")
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class TestHnswPQSearchParams(TestMilvusClientV2Base):
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"""Test search with pagination functionality for HNSW_PQ index"""
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def setup_class(self):
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super().setup_class(self)
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self.collection_name = "TestHnswPQSearchParams" + cf.gen_unique_str("_")
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self.float_vector_field_name = vector_field_name
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self.float_vector_dim = dim
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self.primary_keys = []
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self.enable_dynamic_field = False
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self.datas = []
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@pytest.fixture(scope="class", autouse=True)
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def prepare_collection(self, request):
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"""
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Initialize collection before test class runs
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"""
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client = self._client()
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collection_schema = self.create_schema(client)[0]
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collection_schema.add_field(pk_field_name, DataType.INT64, is_primary=True, auto_id=False)
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collection_schema.add_field(self.float_vector_field_name, DataType.FLOAT_VECTOR, dim=128)
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self.create_collection(client, self.collection_name, schema=collection_schema,
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enable_dynamic_field=self.enable_dynamic_field, force_teardown=False)
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all_data = cf.gen_row_data_by_schema(
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nb=default_nb,
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schema=collection_schema,
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start=0,
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random_pk=False
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)
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self.insert(client, self.collection_name, data=all_data)
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self.primary_keys.extend([i for i in range(default_nb)])
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self.flush(client, self.collection_name)
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# Create HNSW_PQ index
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name=self.float_vector_field_name,
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metric_type="COSINE",
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index_type=index_type,
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params=default_build_params)
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self.create_index(client, self.collection_name, index_params=index_params)
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self.wait_for_index_ready(client, self.collection_name, index_name=self.float_vector_field_name)
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self.load_collection(client, self.collection_name)
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def teardown():
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self.drop_collection(self._client(), self.collection_name)
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request.addfinalizer(teardown)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("params", HNSW_PQ.search_params)
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def test_hnsw_pq_search_params(self, params):
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"""
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Test the search params of HNSW_PQ index
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"""
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client = self._client()
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collection_name = self.collection_name
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nq = 2
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search_vectors = cf.gen_vectors(nq, dim=self.float_vector_dim, vector_data_type=DataType.FLOAT_VECTOR)
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search_params = params.get("params", None)
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if params.get("expected", None) != success:
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self.search(client, collection_name, search_vectors,
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search_params=search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.err_res,
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check_items=params.get("expected"))
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else:
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self.search(client, collection_name, search_vectors,
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search_params=search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": nq,
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"limit": ct.default_limit,
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"pk_name": pk_field_name}) |