AnandInguva commented on code in PR #29564:
URL: https://github.com/apache/beam/pull/29564#discussion_r1420909045
##########
sdks/python/apache_beam/ml/transforms/tft.py:
##########
@@ -95,6 +96,24 @@ def __init__(self, columns: List[str]) -> None:
"Columns are not specified. Please specify the column for the "
" op %s" % self.__class__.__name__)
+ def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
+ from apache_beam.ml.transforms.handlers import TFTProcessHandler
+ params = {}
+ artifact_location = kwargs.get('artifact_location')
+ if not artifact_location:
+ raise RuntimeError(
+ "artifact_location is not specified. Please specify the "
+ "artifact_location for the op %s" % self.__class__.__name__)
+
+ transforms = kwargs.get('transforms')
+ if transforms:
+ params['transforms'] = transforms
Review Comment:
Yes, we are not passing it right now. we can add it later if we need it.
Thanks for catching.
##########
sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py:
##########
@@ -0,0 +1,152 @@
+#
+# 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"]
+
+DEFAULT_TASK_TYPE = "RETRIEVAL_DOCUMENT"
+# TODO: Can this list be automatically pulled from Vertex SDK?
+TASK_TYPE_INPUTS = [
+ "RETRIEVAL_DOCUMENT",
+ "RETRIEVAL_QUERY",
+ "SEMANTIC_SIMILARITY",
+ "CLASSIFICATION",
+ "CLUSTERING"
+]
+_BATCH_SIZE = 5 # Vertex AI limits requests to 5 at a time.
+
+
+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 = DEFAULT_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}")
+ self.task_type = task_type
+ self.title = title
+
+ def run_inference(
+ self,
+ batch: Sequence[str],
+ model: Any,
+ inference_args: Optional[Dict[str, Any]] = None,
+ ) -> Iterable:
+ embeddings = []
+ batch_size = _BATCH_SIZE
+ for i in range(0, len(batch), batch_size):
+ text_batch = batch[i:i + batch_size]
+ text_batch = [
+ TextEmbeddingInput(
+ text=text, title=self.title, task_type=self.task_type)
+ for text in text_batch
+ ]
+ embeddings_batch = model.get_embeddings(text_batch)
+ embeddings.extend([el.values for el in embeddings_batch])
+ return embeddings
+
+ def load_model(self):
+ model = TextEmbeddingModel.from_pretrained(self.model_name)
+ return model
+
+
+class VertexAITextEmbeddings(EmbeddingsManager):
+ def __init__(
+ self,
+ model_name: str,
+ columns: List[str],
+ title: Optional[str] = None,
+ task_type: str = DEFAULT_TASK_TYPE,
+ project: Optional[str] = None,
+ location: Optional[str] = None,
+ credentials: Optional[Credentials] = None,
+ **kwargs):
+ """
+ Embedding Config for Vertex AI Text Embedding models following
+
https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings
# pylint: disable=line-too-long
+ Text Embeddings are generated for a batch of text using the Vertex AI SDK.
+ Embeddings are returned in a list for each text in the batch. Look at
+
https://cloud.google.com/vertex-ai/docs/generative-ai/learn/model-versioning#stable-versions-available.md
# pylint: disable=line-too-long
+ for more information on model versions and lifecycle.
+
+ Args:
+ model_name: The name of the Vertex AI Text Embedding model.
+ columns: The columns containing the text to be embedded.
+ task_type: The downstream task for the embeddings.
+ Valid values are RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT,
+ SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING.
Review Comment:
Added the link
##########
sdks/python/apache_beam/ml/transforms/base.py:
##########
@@ -76,33 +148,52 @@ def __call__(self, data: OperationInputT,
transformed_data = self.apply_transform(data, output_column_name)
return transformed_data
- def get_counter(self):
- """
- Returns the counter name for the operation.
- """
- counter_name = self.__class__.__name__
- return Metrics.counter(MLTransform, f'BeamML_{counter_name}')
-
-class ProcessHandler(Generic[ExampleT, MLTransformOutputT], abc.ABC):
+class ProcessHandler(beam.PTransform[beam.PCollection[ExampleT],
+ beam.PCollection[MLTransformOutputT]],
+ abc.ABC):
"""
Only for internal use. No backwards compatibility guarantees.
"""
@abc.abstractmethod
- def process_data(
- self, pcoll: beam.PCollection[ExampleT]
- ) -> beam.PCollection[MLTransformOutputT]:
+ def append_transform(self, transform: BaseOperation):
"""
- Logic to process the data. This will be the entrypoint in
- beam.MLTransform to process incoming data.
+ Append transforms to the ProcessHandler.
"""
+
+# TODO: Add support for inference_fn
+class EmbeddingsManager(MLTransformProvider):
+ def __init__(
+ self,
+ columns: List[str],
+ *,
+ # common args for all ModelHandlers.
+ load_model_args: Optional[Dict[str, Any]] = None,
+ min_batch_size: Optional[int] = None,
+ max_batch_size: Optional[int] = None,
+ large_model: bool = False,
+ **kwargs):
+ self.load_model_args = load_model_args or {}
+ self.min_batch_size = min_batch_size
+ self.max_batch_size = max_batch_size
+ self.large_model = large_model
+ self.columns = columns
+ self.inference_args = kwargs.pop('inference_args', {})
+
+ if kwargs:
+ _LOGGER.warning("Ignoring the following arguments: %s", kwargs.keys())
+
+ # TODO: Add set_model_handler method.
Review Comment:
Done
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