AnandInguva commented on code in PR #29564:
URL: https://github.com/apache/beam/pull/29564#discussion_r1416277724
##########
sdks/python/apache_beam/ml/transforms/base.py:
##########
@@ -254,3 +371,243 @@ def _increment_counters():
pipeline
| beam.Create([None])
| beam.Map(lambda _: _increment_counters()))
+
+
+class _TransformAttributeManager:
+ """
+ Base class used for saving and loading the attributes.
+ """
+ @staticmethod
+ def save_attributes(artifact_location):
+ """
+ Save the attributes to json file using stdlib json.
+ """
+ raise NotImplementedError
+
+ @staticmethod
+ def load_attributes(artifact_location):
+ """
+ Load the attributes from json file.
+ """
+ raise NotImplementedError
+
+
+class _JsonPickleTransformAttributeManager(_TransformAttributeManager):
+ """
+ Use Jsonpickle to save and load the attributes. Here the attributes refer
+ to the list of PTransforms that are used to process the data.
+
+ jsonpickle is used to serialize the PTransforms and save it to a json file
and
+ is compatible across python versions.
+ """
+ @staticmethod
+ def _is_remote_path(path):
+ is_gcs = path.find('gs://') != -1
+ # TODO: Add support for other remote paths.
+ if not is_gcs and path.find('://') != -1:
+ raise RuntimeError(
+ "Artifact locations are currently supported for only available for "
+ "local paths and GCS paths. Got: %s" % path)
+ return is_gcs
+
+ @staticmethod
+ def save_attributes(
+ ptransform_list,
+ artifact_location,
+ **kwargs,
+ ):
+ if _JsonPickleTransformAttributeManager._is_remote_path(artifact_location):
+ try:
+ options = kwargs.get('options')
+ except KeyError:
+ raise RuntimeError(
+ 'pipeline options are required to save the attributes.'
+ 'in the artifact location %s' % artifact_location)
Review Comment:
Done. Removed try/catch
##########
sdks/python/apache_beam/ml/transforms/embeddings/sentence_transformer.py:
##########
@@ -0,0 +1,125 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__all__ = ["SentenceTransformerEmbeddings"]
+
+from typing import Any
+from typing import Callable
+from typing import Dict
+from typing import List
+from typing import Mapping
+from typing import Optional
+from typing import Sequence
+
+import apache_beam as beam
+from apache_beam.ml.inference.base import ModelHandler
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.transforms.base import EmbeddingsManager
+from apache_beam.ml.transforms.base import _TextEmbeddingHandler
+from sentence_transformers import SentenceTransformer
+
+
+# TODO: Use HuggingFaceModelHandlerTensor once the import issue is fixed.
Review Comment:
Added a todo link https://github.com/apache/beam/issues/29621
##########
sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py:
##########
@@ -0,0 +1,158 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# Vertex AI Python SDK is required for this module.
+# Follow
https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk #
pylint: disable=line-too-long
+# to install Vertex AI Python SDK.
+
+from typing import Any
+from typing import Dict
+from typing import Iterable
+from typing import List
+from typing import Optional
+from typing import Sequence
+
+from google.auth.credentials import Credentials
+
+import apache_beam as beam
+import vertexai
+from apache_beam.ml.inference.base import ModelHandler
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.transforms.base import EmbeddingsManager
+from apache_beam.ml.transforms.base import _TextEmbeddingHandler
+from vertexai.language_models import TextEmbeddingInput
+from vertexai.language_models import TextEmbeddingModel
+
+__all__ = ["VertexAITextEmbeddings"]
+
+TASK_TYPE = "RETRIEVAL_DOCUMENT"
+TASK_TYPE_INPUTS = [
+ "RETRIEVAL_DOCUMENT",
+ "RETRIEVAL_QUERY",
+ "SEMANTIC_SIMILARITY",
+ "CLASSIFICATION",
+ "CLUSTERING"
+]
+
+
+class _VertexAITextEmbeddingHandler(ModelHandler):
+ """
+ Note: Intended for internal use and guarantees no backwards compatibility.
+ """
+ def __init__(
+ self,
+ model_name: str,
+ title: Optional[str] = None,
+ task_type: str = TASK_TYPE,
+ project: Optional[str] = None,
+ location: Optional[str] = None,
+ credentials: Optional[Credentials] = None,
+ ):
+ vertexai.init(project=project, location=location, credentials=credentials)
+ self.model_name = model_name
+ if task_type not in TASK_TYPE_INPUTS:
+ raise ValueError(
+ f"task_type must be one of {TASK_TYPE_INPUTS}, got {task_type}")
Review Comment:
400 EMPTY_TASK cannot be parsed as a valid embedding task type. Valid task
types: [CLASSIFICATION, CLUSTERING, DEFAULT, RETRIEVAL_DOCUMENT,
RETRIEVAL_QUERY, SEMANTIC_SIMILARITY] [while running
'MLTransform/RunInference/BeamML_RunInference']
It errors out but after job submission. I created this so that we get faster
error.
I will see if we could automatically pull the list.
##########
sdks/python/apache_beam/ml/transforms/embeddings/sentence_transformer_test.py:
##########
@@ -0,0 +1,212 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+import apache_beam as beam
+from apache_beam.ml.transforms.base import MLTransform
+
+# pylint: disable=ungrouped-imports
+try:
+ from apache_beam.ml.transforms.embeddings.sentence_transformer import
SentenceTransformerEmbeddings
+except ImportError:
+ SentenceTransformerEmbeddings = None # type: ignore
+
+# pylint: disable=ungrouped-imports
+try:
+ import tensorflow_transform as tft
+ from apache_beam.ml.transforms.tft import ScaleTo01
+except ImportError:
+ tft = None
+
+test_query = "This is a test"
+test_query_column = "feature_1"
+DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+
+
+def get_pipeline_wth_embedding_config(
Review Comment:
Removed this method. +1 if we could use our own ModelHandler but having to
download both pytorch and tensorflow at the same time is a big -1. I will link
an issue to the TODO.
##########
sdks/python/apache_beam/ml/transforms/utils.py:
##########
@@ -28,8 +30,13 @@ class ArtifactsFetcher():
to the TFTProcessHandlers in MLTransform.
"""
def __init__(self, artifact_location):
- self.artifact_location = artifact_location
- self.transform_output = tft.TFTransformOutput(self.artifact_location)
+ files = os.listdir(artifact_location)
+ files.remove(base._ATTRIBUTE_FILE_NAME)
+ if len(files) > 1:
+ raise NotImplementedError(
+ 'Multiple files in artifact location not supported yet.')
Review Comment:
User wouldn't know since we create a random directory inside artifact
location. I added more info on the NotImplementedError. One idea is to
integrate ArtifactsFetcher into MLTransform but can be addressed later
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