no-op

PiperOrigin-RevId: 635878645

tfx/orchestration/datahub_utils.py

@@ -0,0 +1,30 @@
+# Copyright 2024 Google LLC. All Rights Reserved.
+#
+# Licensed 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.
+"""Utils to log Tflex/MLMD entities."""
+from typing import Optional
+
+from tfx.orchestration.experimental.core import task as task_lib
+from tfx.utils import typing_utils
+
+from ml_metadata.proto import metadata_store_pb2
+
+
+def log_node_execution(
+    execution: metadata_store_pb2.Execution,
+    task: Optional[task_lib.ExecNodeTask] = None,
+    output_artifacts: Optional[typing_utils.ArtifactMultiMap] = None,
+):
+  """Logs a Tflex node execution and its input/output artifacts."""
+  del execution, task, output_artifacts
+  return

tfx/orchestration/experimental/core/post_execution_utils.py

@@ -92,7 +92,8 @@ def _update_state(
           execution_id=task.execution_id,
           contexts=task.contexts,
           output_artifacts=task.output_artifacts,
-          executor_output=executor_output)
+          executor_output=executor_output,
+          task=task)
     garbage_collection.run_garbage_collection_for_node(mlmd_handle,
                                                        task.node_uid,
                                                        task.get_node())
@@ -125,7 +126,8 @@ def _update_state(
           mlmd_handle,
           execution_id=task.execution_id,
           contexts=task.contexts,
-          output_artifacts=output_artifacts)
+          output_artifacts=output_artifacts,
+          task=task)
   elif isinstance(result.output, ts.ResolverNodeOutput):
     resolved_input_artifacts = result.output.resolved_input_artifacts
     # TODO(b/262040844): Instead of directly using the context manager here, we

tfx/orchestration/portable/execution_publish_utils.py

@@ -12,12 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Portable library for registering and publishing executions."""
+
+import logging
 from typing import Mapping, Optional, Sequence
 import uuid
 
 from tfx import types
 from tfx.orchestration import data_types_utils
 from tfx.orchestration import metadata
+from tfx.orchestration.experimental.core import task as task_lib
+from tfx.orchestration import datahub_utils
 from tfx.orchestration.portable import merge_utils
 from tfx.orchestration.portable.mlmd import execution_lib
 from tfx.proto.orchestration import execution_result_pb2
@@ -75,6 +79,7 @@ def publish_succeeded_execution(
     contexts: Sequence[metadata_store_pb2.Context],
     output_artifacts: Optional[typing_utils.ArtifactMultiMap] = None,
     executor_output: Optional[execution_result_pb2.ExecutorOutput] = None,
+    task: Optional[task_lib.ExecNodeTask] = None,
 ) -> tuple[
     Optional[typing_utils.ArtifactMultiMap],
     metadata_store_pb2.Execution,
@@ -85,6 +90,9 @@ def publish_succeeded_execution(
   will also merge the executor produced info into system generated output
   artifacts. The `last_know_state` of the execution will be changed to
   `COMPLETE` and the output artifacts will be marked as `LIVE`.
+  This method will also publish the execution and its input/output artifacts to
+  Datahub in best-effort mode if `enable_datahub_logging` in
+  TflexProjectPlatformConfig is set to True.
 
   Args:
     metadata_handle: A handler to access MLMD.
@@ -95,11 +103,12 @@ def publish_succeeded_execution(
       event with type OUTPUT.
     executor_output: Executor outputs. `executor_output.output_artifacts` will
       be used to update system-generated output artifacts passed in through
-      `output_artifacts` arg. There are three contraints to the update: 1. The
+      `output_artifacts` arg. There are three constraints to the update: 1. The
       keys in `executor_output.output_artifacts` are expected to be a subset of
       the system-generated output artifacts dict. 2. An update to a certain key
       should contains all the artifacts under that key. 3. An update to an
       artifact should not change the type of the artifact.
+    task: the task that just completed for the given node execution.
 
   Returns:
     The tuple containing the maybe updated output_artifacts (note that only
@@ -108,7 +117,14 @@ def publish_succeeded_execution(
     execution.
   Raises:
     RuntimeError: if the executor output to a output channel is partial.
+    ValueError: if `execution_id` is inconsistent with `task`.execution_id.
   """
+  if task and task.execution_id != execution_id:
+    raise ValueError(
+        f'Task execution_id {task.execution_id} does not match MLMD execution'
+        f' id {execution_id}'
+    )
+
   unpacked_output_artifacts = (
       None  # pylint: disable=g-long-ternary
       if executor_output is None
@@ -155,6 +171,13 @@ def publish_succeeded_execution(
       output_artifacts=output_artifacts_to_publish,
   )
 
+  try:
+    datahub_utils.log_node_execution(
+        execution, task, output_artifacts_to_publish
+    )
+  except Exception:  # pylint: disable=broad-except
+    logging.exception('Failed to log node execution.')
+
   return output_artifacts_to_publish, execution
 
 

tfx/orchestration/portable/execution_publish_utils_test.py

@@ -13,11 +13,14 @@
 # limitations under the License.
 """Tests for tfx.orchestration.portable.execution_publish_utils."""
 import copy
+from unittest import mock
 
 from absl.testing import parameterized
 import tensorflow as tf
 from tfx import version
 from tfx.orchestration import metadata
+from tfx.orchestration.experimental.core import task as task_lib
+from tfx.orchestration import datahub_utils
 from tfx.orchestration.portable import execution_publish_utils
 from tfx.orchestration.portable import outputs_utils
 from tfx.orchestration.portable.mlmd import context_lib
@@ -33,13 +36,63 @@
 from ml_metadata.proto import metadata_store_pb2
 
 
+_DEFAULT_EXECUTOR_OUTPUT_URI = '/fake/path/to/executor_output.pb'
+_DEFAULT_NODE_ID = 'example_node'
+_DEFAULT_OWNER = 'owner'
+_DEFAULT_PROJECT_NAME = 'project_name'
+_DEFAULT_PIPELINE_NAME = 'pipeline_name'
+_DEFAULT_PIPELINE_RUN_ID = 'run-123'
+_DEFAULT_TEMP_DIR = '/fake/path/to/tmp_dir/'
+_DEFAULT_STATEFUL_WORKING_DIR = '/fake/path/to/stateful_working_dir/'
+
+
+def _create_pipeline() -> pipeline_pb2.Pipeline:
+  deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
+  pipeline = pipeline_pb2.Pipeline(
+      pipeline_info=pipeline_pb2.PipelineInfo(id=_DEFAULT_PIPELINE_NAME),
+      nodes=[
+          pipeline_pb2.Pipeline.PipelineOrNode(
+              pipeline_node=pipeline_pb2.PipelineNode(
+                  node_info=pipeline_pb2.NodeInfo(id=_DEFAULT_NODE_ID)
+              ),
+          ),
+      ],
+  )
+  pipeline.deployment_config.Pack(deployment_config)
+  return pipeline
+
+
+def _create_exec_node_task(
+    pipeline: pipeline_pb2.Pipeline,
+    execution_id: int,
+) -> task_lib.ExecNodeTask:
+  return task_lib.ExecNodeTask(
+      pipeline=pipeline,
+      node_uid=task_lib.NodeUid(
+          pipeline_uid=task_lib.PipelineUid.from_pipeline(pipeline),
+          node_id=_DEFAULT_NODE_ID,
+      ),
+      execution_id=execution_id,
+      contexts=[],
+      exec_properties={},
+      input_artifacts={},
+      output_artifacts={},
+      executor_output_uri=_DEFAULT_EXECUTOR_OUTPUT_URI,
+      stateful_working_dir=_DEFAULT_STATEFUL_WORKING_DIR,
+      tmp_dir=_DEFAULT_TEMP_DIR,
+  )
+
+
 class ExecutionPublisherTest(test_case_utils.TfxTest, parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
     self._connection_config = metadata_store_pb2.ConnectionConfig()
     self._connection_config.sqlite.SetInParent()
     self._execution_type = metadata_store_pb2.ExecutionType(name='my_ex_type')
+    self._mock_log_node_execution = self.enter_context(
+        mock.patch.object(datahub_utils, 'log_node_execution')
+    )
 
   def _generate_contexts(self, metadata_handle):
     context_spec = pipeline_pb2.NodeContexts()
@@ -191,13 +244,15 @@ def testPublishSuccessfulExecution(self):
             value {int_value: 1}
           }
           """, executor_output.output_artifacts[output_key].artifacts.add())
+      task = _create_exec_node_task(_create_pipeline(), execution_id)
       output_dict, execution = (
           execution_publish_utils.publish_succeeded_execution(
               m,
               execution_id,
               contexts,
               {output_key: [output_example]},
               executor_output,
+              task,
           )
       )
       self.assertProtoPartiallyEquals(
@@ -283,6 +338,11 @@ def testPublishSuccessfulExecution(self):
           self.assertCountEqual([c.id for c in contexts], [
               c.id for c in m.store.get_contexts_by_artifact(output_example.id)
           ])
+      self._mock_log_node_execution.assert_called_once_with(
+          execution,
+          task,
+          output_dict,
+      )
 
   def testPublishSuccessfulExecutionWithRuntimeResolvedUri(self):
     with metadata.Metadata(connection_config=self._connection_config) as m:
@@ -307,10 +367,17 @@ def testPublishSuccessfulExecutionWithRuntimeResolvedUri(self):
               value {{int_value: 1}}
             }}
             """, executor_output.output_artifacts[output_key].artifacts.add())
-
-      output_dict, _ = execution_publish_utils.publish_succeeded_execution(
-          m, execution_id, contexts, {output_key: [output_example]},
-          executor_output)
+      task = _create_exec_node_task(_create_pipeline(), execution_id)
+      output_dict, execution = (
+          execution_publish_utils.publish_succeeded_execution(
+              m,
+              execution_id,
+              contexts,
+              {output_key: [output_example]},
+              executor_output,
+              task,
+          )
+      )
       self.assertLen(output_dict[output_key], 2)
       self.assertEqual(output_dict[output_key][0].uri, '/examples_uri/1')
       self.assertEqual(output_dict[output_key][1].uri, '/examples_uri/2')
@@ -337,6 +404,11 @@ def testPublishSuccessfulExecutionWithRuntimeResolvedUri(self):
             """,
             event,
             ignored_fields=['milliseconds_since_epoch'])
+      self._mock_log_node_execution.assert_called_once_with(
+          execution,
+          task,
+          output_dict,
+      )
 
   def testPublishSuccessfulExecutionOmitsArtifactIfNotResolvedDuringRuntime(
       self):
@@ -366,12 +438,26 @@ def testPublishSuccessfulExecutionOmitsArtifactIfNotResolvedDuringRuntime(
             value {{int_value: 1}}
           }}
           """, executor_output.output_artifacts['key1'].artifacts.add())
-      output_dict, _ = execution_publish_utils.publish_succeeded_execution(
-          m, execution_id, contexts, original_artifacts, executor_output)
+      task = _create_exec_node_task(_create_pipeline(), execution_id)
+      output_dict, execution = (
+          execution_publish_utils.publish_succeeded_execution(
+              m,
+              execution_id,
+              contexts,
+              original_artifacts,
+              executor_output,
+              task,
+          )
+      )
       self.assertEmpty(output_dict['key1'])
       self.assertNotEmpty(output_dict['key2'])
       self.assertLen(output_dict['key2'], 1)
       self.assertEqual(output_dict['key2'][0].uri, '/foo/bar')
+      self._mock_log_node_execution.assert_called_once_with(
+          execution,
+          task,
+          output_dict,
+      )
 
   def testPublishSuccessExecutionFailNewKey(self):
     with metadata.Metadata(connection_config=self._connection_config) as m:
@@ -418,14 +504,15 @@ def testPublishSuccessExecutionExecutorEditedOutputDict(self):
             value {int_value: 2}
           }
           """, executor_output.output_artifacts[output_key].artifacts.add())
-
+      task = _create_exec_node_task(_create_pipeline(), execution_id)
       output_dict, execution = (
           execution_publish_utils.publish_succeeded_execution(
               m,
               execution_id,
               contexts,
               {output_key: [output_example]},
               executor_output,
+              task,
           )
       )
       self.assertProtoPartiallyEquals(
@@ -541,6 +628,11 @@ def testPublishSuccessExecutionExecutorEditedOutputDict(self):
               output_example.get_string_custom_property(
                   artifact_utils.ARTIFACT_TFX_VERSION_CUSTOM_PROPERTY_KEY),
               version.__version__)
+      self._mock_log_node_execution.assert_called_once_with(
+          execution,
+          task,
+          output_dict,
+      )
 
   def testPublishSuccessExecutionFailChangedType(self):
     with metadata.Metadata(connection_config=self._connection_config) as m: