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| import typing | |
| import asyncio | |
| from typing import Any, Iterable, List | |
| from nextdoor.sitevars import sitevars_client | |
| from nextdoor.util.featurestore.utils.fetch_features import fetch_features_for_ids | |
| from protond.featurestore.business.general_pb2 import General | |
| from protond.featurestore.business.offline_general_pb2 import OfflineGeneral | |
| from protond.featurestore.featurestoresvc import featurestore_pb2 | |
| from protond.featurestore.featurestoresvc import embedding_transformation_pb2 as et_pb2 | |
| from nextdoor.util.featurestore.utils import fetch_embeddings | |
| AD_LI_FEATURE_GROUP = "adlineitem_embeddings_v1" | |
| USER_FEATURE_GROUP = "user_adlineitem_embeddings_v1" | |
| EMBEDDING_DIM = 300 | |
| BATCH_SIZE = 20 | |
| LI_SIZE = 10000 | |
| def _remove_blocklisted_features(features: Iterable[str]) -> List[str]: | |
| blocklist: List[str] = sitevars_client.sitevars_client().fetch_sitevar( | |
| "ads_blocklisted_fs_features", [] | |
| ) | |
| return [x for x in features if x not in set(blocklist)] | |
| async def fetch_raw_user_features(user_ids: Iterable[str]) -> featurestore_pb2.MonoRequestReply: | |
| features = _remove_blocklisted_features([USER_FEATURE_GROUP]) | |
| return ((await fetch_features_for_ids([str(x) for x in user_ids], features)) if features else featurestore_pb2.MonoRequestReply()) | |
| async def fetch_raw_line_item_features(line_item_ids: Iterable[str]) -> featurestore_pb2.MonoRequestReply: | |
| features = _remove_blocklisted_features([AD_LI_FEATURE_GROUP]) | |
| return ((await fetch_features_for_ids([str(x) for x in line_item_ids], features)) if features else featurestore_pb2.MonoRequestReply()) | |
| ad_embedding = asyncio.run(fetch_raw_line_item_features([ad_line_item_id])) | |
| user_embedding = asyncio.run(fetch_raw_user_features([user_id])) | |
| #Injection | |
| from protond.featurestore.featurestoresvc import featurestore_pb2 | |
| import numpy as np | |
| import uuid | |
| import asyncio | |
| from nextdoor.util import aio_batcher | |
| from protond.featurestore.adlineitem.embeddings_pb2 import EmbeddingV1 | |
| from protond.featurestore.user.embeddings_pb2 import AdlineitemEmbeddingV1 | |
| from nextdoor.util.featurestore import featurestore_client_async | |
| fs_client = featurestore_client_async.featurestore_client_async() | |
| # TODO introduce counter | |
| def insert_li_embedding(ad_line_item_id): | |
| e = np.random.uniform(0.0, 1.0,(1 , EMBEDDING_DIM))[0] | |
| embedding_v1 = EmbeddingV1(id=ad_line_item_id, embedding=e, dimension=EMBEDDING_DIM) | |
| req = featurestore_pb2.MonoRequestReply() | |
| req.included_feature_groups.append(AD_LI_FEATURE_GROUP) | |
| getattr(req, AD_LI_FEATURE_GROUP)[ad_line_item_id].CopyFrom(embedding_v1) | |
| aio_batcher.run_until_complete(fs_client.ingest_features(req, ttl=14 * 24 * 3600)) | |
| def insert_user_embedding(user_id): | |
| e = np.random.uniform(0.0, 1.0,(1 , EMBEDDING_DIM))[0] | |
| user_embedding_v1 = AdlineitemEmbeddingV1(id=user_id, embedding=e, dimension=EMBEDDING_DIM) | |
| req = featurestore_pb2.MonoRequestReply() | |
| req.included_feature_groups.append(USER_FEATURE_GROUP) | |
| getattr(req, USER_FEATURE_GROUP)[user_id].CopyFrom(user_embedding_v1) | |
| aio_batcher.run_until_complete(fs_client.ingest_features(req, ttl=14 * 24 * 3600)) | |
| ad_line_item_ids = [] | |
| max_line_items = 10000 | |
| for i in range(max_line_items): | |
| ad_line_item_id = str(uuid.uuid4()) | |
| insert_li_embedding(ad_line_item_id) | |
| ad_line_item_ids.append(ad_line_item_id) | |
| print(len(ad_line_item_ids)) | |
| user_id = 'ca77ed49-9e74-46c5-9d6e-f2622e453db7' | |
| insert_user_embedding(user_id) | |
| def prep_embedding_transformation( | |
| user_id: str, | |
| line_item_ids: List[str], | |
| ) -> List[et_pb2.EmbeddingTransformation]: | |
| num_transformations = len(line_item_ids) | |
| embedding_transformations: List[Any] = [None] * num_transformations | |
| user_emb_key = et_pb2.EmbeddingKey( | |
| name=USER_FEATURE_GROUP, id=user_id | |
| ) | |
| idx = 0 | |
| for line_item_id in line_item_ids: | |
| li_emb_key = et_pb2.EmbeddingKey(name=AD_LI_FEATURE_GROUP, id=line_item_id) | |
| # user x line item dot product | |
| embedding_transformations[idx] = et_pb2.EmbeddingTransformation( | |
| transformation_type=et_pb2.EmbeddingTransformationType.DOT_PRODUCT, | |
| embeddings=[user_emb_key, li_emb_key], | |
| ) | |
| idx += 1 | |
| return embedding_transformations | |
| filename = '/tools/iris/transfer/ad_line_item_ids.tsv' | |
| def read_lines(filename): | |
| line_item_ids = [] | |
| with open(filename) as f: | |
| lines = f.readlines() | |
| [line_item_ids.append(l.strip()) for l in lines] | |
| return line_item_ids | |
| line_item_ids = read_lines(filename)[:LI_SIZE] | |
| embedding_transformations = prep_embedding_transformation(user_id, line_item_ids) | |
| # asyncio | |
| transformation_values = asyncio.run(fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| )) | |
| # no asyncio | |
| transformation_values = await fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| ) | |
| # for loop with batch_size > 100 and LI_SIZE in thousannds with cold start | |
| batch_line_item_ids = line_item_ids[5000:8000] | |
| BATCH_SIZE = 100 | |
| embedding_transformations = prep_embedding_transformation(user_id, batch_line_item_ids) | |
| transformation_values = await fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| ) | |
| user_li_dot_product_map: Dict[str, float] = {} | |
| if transformation_values: | |
| for line_item_id, transformation_value in zip(line_item_ids, transformation_values): | |
| user_li_dot_product_map[line_item_id] = transformation_value | |
| len(user_li_dot_product_map.keys()) |
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| import typing | |
| import asyncio | |
| from typing import Any, Iterable, List | |
| from nextdoor.sitevars import sitevars_client | |
| from nextdoor.util.featurestore.utils.fetch_features import fetch_features_for_ids | |
| from protond.featurestore.business.general_pb2 import General | |
| from protond.featurestore.business.offline_general_pb2 import OfflineGeneral | |
| from protond.featurestore.featurestoresvc import featurestore_pb2 | |
| from protond.featurestore.featurestoresvc import embedding_transformation_pb2 as et_pb2 | |
| from nextdoor.util.featurestore.utils import fetch_embeddings | |
| AD_LI_FEATURE_GROUP = "adlineitem_embeddings_v1" | |
| USER_FEATURE_GROUP = "user_embedding" | |
| EMBEDDING_DIM = 512 | |
| BATCH_SIZE = 20 | |
| LI_SIZE = 10000 | |
| def _remove_blocklisted_features(features: Iterable[str]) -> List[str]: | |
| blocklist: List[str] = sitevars_client.sitevars_client().fetch_sitevar( | |
| "ads_blocklisted_fs_features", [] | |
| ) | |
| return [x for x in features if x not in set(blocklist)] | |
| async def fetch_raw_user_features(user_ids: Iterable[str]) -> featurestore_pb2.MonoRequestReply: | |
| features = _remove_blocklisted_features([USER_FEATURE_GROUP]) | |
| return ((await fetch_features_for_ids([str(x) for x in user_ids], features)) if features else featurestore_pb2.MonoRequestReply()) | |
| async def fetch_raw_line_item_features(line_item_ids: Iterable[str]) -> featurestore_pb2.MonoRequestReply: | |
| features = _remove_blocklisted_features([AD_LI_FEATURE_GROUP]) | |
| return ((await fetch_features_for_ids([str(x) for x in line_item_ids], features)) if features else featurestore_pb2.MonoRequestReply()) | |
| ad_embedding = asyncio.run(fetch_raw_line_item_features([ad_line_item_id])) | |
| user_embedding = asyncio.run(fetch_raw_user_features([user_id])) | |
| #Injection | |
| from protond.featurestore.featurestoresvc import featurestore_pb2 | |
| import numpy as np | |
| import uuid | |
| import asyncio | |
| from nextdoor.util import aio_batcher | |
| from protond.featurestore.adlineitem.embeddings_pb2 import EmbeddingV1 | |
| from protond.featurestore.user.embeddings_pb2 import EmbeddingMultiLingualDistilBert | |
| from nextdoor.util.featurestore import featurestore_client_async | |
| fs_client = featurestore_client_async.featurestore_client_async() | |
| # TODO introduce counter | |
| def insert_li_embedding(ad_line_item_id): | |
| e = np.random.uniform(0.0, 1.0,(1 , EMBEDDING_DIM))[0] | |
| embedding_v1 = EmbeddingV1(id=ad_line_item_id, embedding=e, dimension=EMBEDDING_DIM) | |
| req = featurestore_pb2.MonoRequestReply() | |
| req.included_feature_groups.append(AD_LI_FEATURE_GROUP) | |
| getattr(req, AD_LI_FEATURE_GROUP)[ad_line_item_id].CopyFrom(embedding_v1) | |
| aio_batcher.run_until_complete(fs_client.ingest_features(req, ttl=14 * 24 * 3600)) | |
| def insert_user_embedding(user_id): | |
| e = np.random.uniform(0.0, 1.0,(1 , EMBEDDING_DIM))[0] | |
| user_embedding_v1 = EmbeddingMultiLingualDistilBert(id=user_id, embedding=e, dimension=EMBEDDING_DIM) | |
| req = featurestore_pb2.MonoRequestReply() | |
| req.included_feature_groups.append(USER_FEATURE_GROUP) | |
| getattr(req, USER_FEATURE_GROUP)[user_id].CopyFrom(user_embedding_v1) | |
| aio_batcher.run_until_complete(fs_client.ingest_features(req, ttl=14 * 24 * 3600)) | |
| ad_line_item_ids = [] | |
| max_line_items = 10000 | |
| for i in range(max_line_items): | |
| ad_line_item_id = str(uuid.uuid4()) | |
| insert_li_embedding(ad_line_item_id) | |
| ad_line_item_ids.append(ad_line_item_id) | |
| print(len(ad_line_item_ids)) | |
| user_id = 'ca77ed49-9e74-46c5-9d6e-f2622e453db7' | |
| insert_user_embedding(user_id) | |
| def prep_embedding_transformation( | |
| user_id: str, | |
| line_item_ids: List[str], | |
| ) -> List[et_pb2.EmbeddingTransformation]: | |
| num_transformations = len(line_item_ids) | |
| embedding_transformations: List[Any] = [None] * num_transformations | |
| user_emb_key = et_pb2.EmbeddingKey( | |
| name=USER_FEATURE_GROUP, id=user_id | |
| ) | |
| idx = 0 | |
| for line_item_id in line_item_ids: | |
| li_emb_key = et_pb2.EmbeddingKey(name=AD_LI_FEATURE_GROUP, id=line_item_id) | |
| # user x line item dot product | |
| embedding_transformations[idx] = et_pb2.EmbeddingTransformation( | |
| transformation_type=et_pb2.EmbeddingTransformationType.DOT_PRODUCT, | |
| embeddings=[user_emb_key, li_emb_key], | |
| ) | |
| idx += 1 | |
| return embedding_transformations | |
| filename = '/tools/iris/transfer/ad_line_item_ids.tsv' | |
| def read_lines(filename): | |
| line_item_ids = [] | |
| with open(filename) as f: | |
| lines = f.readlines() | |
| [line_item_ids.append(l.strip()) for l in lines] | |
| return line_item_ids | |
| line_item_ids = read_lines(filename)[:LI_SIZE] | |
| embedding_transformations = prep_embedding_transformation(user_id, line_item_ids) | |
| # asyncio | |
| transformation_values = asyncio.run(fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| )) | |
| # no asyncio | |
| transformation_values = await fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| ) | |
| # for loop with batch_size > 100 and LI_SIZE in thousannds with cold start | |
| batch_line_item_ids = line_item_ids[5000:8000] | |
| BATCH_SIZE = 100 | |
| embedding_transformations = prep_embedding_transformation(user_id, batch_line_item_ids) | |
| transformation_values = await fetch_embeddings.get_transformed_embeddings_in_batch( | |
| transformations=embedding_transformations, default_value=0.0, batch_size=BATCH_SIZE | |
| ) | |
| user_li_dot_product_map: Dict[str, float] = {} | |
| if transformation_values: | |
| for line_item_id, transformation_value in zip(line_item_ids, transformation_values): | |
| user_li_dot_product_map[line_item_id] = transformation_value | |
| len(user_li_dot_product_map.keys()) | |
| user_id = ‘50720910’ | |
| insert_user_embedding(user_id) |
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